Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Computation Offloading and Resource Management for Vehicle Platoon: A Deep Reinforcement Learning Approach

On the Internet of Vehicles, the computation capability of individual vehicle is restricted to finish upcoming vehicular computational tasks. Fortunately, vehicular edge computing (VEC) has been proposed to meet the pressing resource demands of vehicular services. In this paper, a cooperative computation offloading and resource management approach is proposed, in which the generated tasks can be offloaded to vehicles and roadside units that are relatively close to the computing vehicle through vehicle-to-vehicle communication (V2V) and vehicle-to-roadside communication (V2R). Taking into account the mobility of vehicles and the time-variety of computation resources for available nodes in the vehicular network, the system model, task delay, task energy consumption and optimization objectives are analysed respectively. Moreover, this optimization problem is solved by a deep reinforcement learning algorithm (DRL), namely Asynchronous Advantage Actor-Critic (A3C). Validated through simulations, the proposed approach shows superiority to other offloading strategies in terms of the calculated reward, task delay, and energy consumption.
Hysteresis and multistability in networks of bistable stochastic elements with global interaction

We demonstrate the existence of the first-order phase transitions and hysteresis in a network of bistable stochastic elements with global interaction subject to additive white noise. Using the Fokker–Planck equation approach, we present a method which allows one to use a continuation technique (AUTO) to follow the stationary one-particle distribution density in the space of system parameters. In addition, the Gaussian approximation is employed to compute the loci of the bifurcation points.
Image-Based Recognition of Fruit Bearing Rambutan (Nephelium Lappaceum) Using Deep Learning

Rambutan is one of the world's most essential fruits because of its nutritional and medicinal benefits. However, 40 to 60 percent of the common cultivars grown from seeds are male and do not bear fruit. The worst part is that it takes four to five years from planting for this to be discovered when the plant is already in the flowering stage. This study leverages transfer learning to fine-tune three pre-trained deep learning models, notably AlexNet, VGG16, and GoogLeNet, to enable the recognition of fruit-bearing rambutan at early stages based on their leaf images. Overall, fine-tuning deep learning models to classify the sexuality of a plant using a novel dataset is feasible, evidenced by more than 95 % accuracy across all models, even to a limited number of training images, with VGG 16 exhibited the highest with 98.40 %.
HIV Viral Dynamic Models With Censoring and Informative Dropouts

HIV viral dynamic models have received much interest in the literature in recent years. These models are useful for modeling the viral load trajectories during an anti-HIV treatment and for evaluating the efficacy of the treatment. In AIDS studies, patients may drop out of the study early due possibly to drug side-effects, and viral load measurements often have a lower limit of detection. Statistical analyses are therefore complicated by the censoring and dropouts in the data. We propose a joint likelihood method which addresses censoring and dropouts in a mixed effects model simultaneously. A real AIDS dataset is analyzed, and a simulation is conducted to evaluate the proposed method.
Bayesian Network for Base Station Energy Consumption Data Completion

Base station energy consumption data loss is randomly caused when collecting and saving various data from massive base stations. At the same time, after the AI energy-saving system issued the 5G deep sleep strategy, the energy consumption of the base station is greatly reduced, but it is necessary to restore the real energy consumption during the energy-saving period. The integrity of base station energy consumption data plays a key role in the assessment of AI base station energy-saving system. The assessment of base station energy consumption can be used as an important feedback content for the AI training model (for prediction, decision-making, etc.) that realizes automatic energy saving for base station. Existing deep learning solutions or GAN (Generative adversarial network) are difficult to meet the requirements of a large amount of effective training data. In this paper, a method based on Gaussian Bayesian network high-dimensional tensor decomposition (GBHTD) is proposed. Extensive empirical studies on multiple areas base station energy consumption datasets demonstrate that GBHTD can outperform state-of-the-art baseline methods in base station energy consumption data completion performance. Specifically, GBHTD decreases MAPE (Mean absolute percentage error) by up to 11%, reducing training time by up to 99% compared to the baselines.
On-the-Fly Symbolic Synthesis With Memory Reduction Guarantees

We present novel on-the-fly abstraction-based controller synthesis algorithms for two fundamental specifications—invariance and optimal reachability. In contrast to existing methods, the algorithms we propose do not require precomputation or storage of any large abstraction parts. This translates to orders of magnitude of reduction in memory consumption compared to standard methods. Moreover, our algorithms fully apply to general nonlinear systems and provide controllers that do not posses any additional conservatism. We demonstrate the successful synthesis of controllers for a problem that was previously unsolvable due to excessive memory and time consumption.
Comparison of Deep Learning Networks for Source Camera Identification

Digital Image Forensics is getting more attention in the research area as the need for confirming the authenticity of images is increasing with the increased use of information sharing through social media. Source Camera Identification (SCI) is an important Digital Image Forensic technique to ensure the identity of the source/camera which has captured a particular image. SCI methods are hot topics in research area for two decades, but with the advancement of Convolutional Neural Networks for Computer Vision applications, so many deep learning based techniques for Source Camera Identification is proposed. This paper aims to review the important deep learning networks for Source Camera Identification. In this paper, we describe the basic structure of the algorithm for Source Camera Identification and present a detailed comparison of various deep learning based techniques on the basis of accuracy, number of cameras and dataset used. Also a source camera device identification technique is implemented using VGG16 model witha an accuracy of 87.16% which is higher than the state of the art methods.
A Detecting Method Against Cross-Container Spectre Attacks

Spectre attacks exploit vulnerabilities in CPU architecture, affect almost all processors, and pose a threat to user information security. In the cloud platform, the isolation mechanism of the container can only ensure a limited level of security. Due to the sharing of hardware resources between containers, Spectre attacks can be implemented in cross-container way. In order to mitigate the damage of the attacks, we propose a method to detect cross-container Spectre attacks. When programs to be detected are running in the container, we use hardware performance counters to collect data. Then the random forest algorithm is used to select the most obvious features of Spectre attacks. Finally, a neural network is built to analyze the data to identify Spectre attacks. Experiments show that the system can detect Spectre attacks in containers efficiently and achieve better effect than the existing detection methods.
Research on Motion Simulation of Stacking Robot Workstation Based on RobotStudio

In view of the problems of small paper packaging boxes in factory packing and transportation, this paper proposes a method to use the simulation software RobotStudio to carry out motion simulation on the design scheme in the Stacking Robot Workstation and optimize the mechanical structure. First, this paper uses SolidWorks modeling software to design the virtual prototype of the stacking robot workstation, import the model into RobotStudio to complete the space layout, and complete the intelligent setting of pneumatic gripper and conveyor belt. Secondly, this paper creates the teaching point, generates the path, establishes the communication connection between the robot and the intelligent components, and realizes the simulation of the stacking robot workstation.According to the simulation results, the position coordinates of the robot and the structure of the pneumatic gripper are optimized, which makes the design of the workstation more reasonable. This method can also be used for the simulation of industrial robot production lines for other purposes.
AreaHash: A Balanced and fully scalable consistency hashing algorithm

Distributed systems use consistent hashing algorithms to solve load balancing problems because of their good balance, minimal interruptions, and high query rates. However, existing algorithms fail to maintain consistency under changes, require high memory for high query rates, or have poor scaling. Therefore, we propose consistent AreaHash, which virtualizes the hash space into a ring. The logically ordered ring nodes occupy the same area, allowing the AreaHash to expand the nodes without any change. The key calculated using the hashing algorithm can be mapped to a node by a simple shift calculation, reducing memory requirements. We make a reasonable theoretical analysis and perform high-level algorithm implementation with a memory footprint of only a few bytes per node. Experimental results indicate that AreaHash achieves a lookup rate of more than 40 million keys per second, even when the number of nodes on a single CPU exceeds one million. In addition, a lookup rate of more than 20 million keys per second is achieved when more than half of the nodes in the cluster are down.
All-to-All Routing Algorithm for Galaxyfly Networks

Interconnection networks play a critical role in the architecture of high-performance computing (HPC) systems. The Galaxyfly network has emerged as a flexible-radix low-diameter topology which is designed for HPC. Considering the importance of collective communication in HPC, in this paper, we present two different all-to-all routing algorithms for the Galaxyfly network. The first one complies with the rule of supernode-first, and it takes full advantage of the network resources in the routing process. As for the second one, we abide by the rule of router-first so that packets can be delivered to other routers within the same supernode as early as possible. The proposed all-to-all routing algorithms use breadth-first and recursive approaches for collecting and sending packets. To our knowledge, this is the first all-to-all routing work for Galaxyfly networks.
Over-reflection and shear instability in a shallow-water model

The characteristics of shallow-water waves in a linear shear flow are studied, and the relationship between waves and unstable modes is examined. Numerical integration of the linear shallow-water equations shows that over-reflection occurs when a wave packet is incident at the turning surface. This phenomenon can be explained by the conservation of momentum as discussed by Acheson (1976). The unstable modes of linear shear flow in a shallow water found by Satomura (1981) are described in terms of the properties of wave propagation as proposed by Lindzen and others. Ripas's (1983) theorem, which is the sufficient condition for stability of flows in shallow water, is also related to the wave geometry. The Orr mechanism, which is proposed by Lindzen (1988) as the primary mechanism of wave amplification, cannot explain the over-reflection of shallow-water waves. The amplification of these waves occurs in the opposite sense to that of Orr's solution.
A Thermal-Aware Approach for DVFS-enabled Multi-core Architectures

Reducing thermal dissipation is vital for modern multi-core architectures to meet increasing computational demands. In this paper, we consider non-preemptive periodic tasks at two importance levels - Safety-Critical tasks (SC-tasks) and Best-Effort tasks (BE-tasks) - executing on a homogeneous multi-core processor with DVFS capabilities under thermal-aware design. We assume that tasks are scheduled according to a fully partitioned Fixed-Task-Priority (FTP) scheduler. Then, we propose two main contributions: (1) a new scheduling scheme, called NP-SafeSC, which reduces the responsiveness of SC-tasks as much as possible by procrastinating the execution of some BE-tasks; and (2) a new framework, called NP-ThermCare, that allows controlling both processor activity and the triggering of the cooling mechanism so that timing and thermal requirements are met. We provide a thorough analysis of our solutions, validate the results and evaluate their performance against a real-world use-case and intensive simulations. Our approach shows a consistent improvement of NP-SafeSC over NP-COIN in the responsiveness of all SC-tasks on each core. In particular, the improvement for the SC-task with the lowest priority reaches 39.53% for the real-world use-case and 45.17% for the simulations.
RBCP: A Reputation-based Blockchain Consensus Protocol for Internet of Things

The application of blockchain in the Internet of Things (IoT) solves the problem of centralization, enables data interaction of IoT nodes in untrusted environments. However, most of the blockchain consensus protocols are computationally expensive and poorly scalable, which cannot meet the current demand for low power consumption and low latency of IoT devices. Meanwhile, IoT nodes suffer from many malicious attacks. An effective reputation evaluation scheme is valuable for establishing a trusted, secure IoT environment with certain applications. In this paper, we propose RBCP, a novel reputation-based blockchain consensus protocol, and apply it to the IoT. The protocol divides IoT nodes into multiple regions, and consensus nodes in each region are selected based on real-time updated reputation values, which are used to participate in light PoW consensus and finally propose blocks. Then, we model the deposits and profits of the nodes as Stackelberg game and analyze the relationship between them. To evaluate our proposed protocol, we compare it with existing consensus protocols through several experiments. Our experimental results show that RBCP has advantages in terms of TPS, consensus time, and communication consumption, proving that it has low latency and less consensus time under the premise of sufficient security.
IRGM: An Integrated RNN-GRU Model for Stock Market Price Prediction

Stock market price prediction has been very challenging because of the non-linearity of such a dataset. Several recent research studies have demonstrated the value of accurate prediction as a motivator for investors, researchers, and market analysts, regardless of market trend. Traditional approaches such as Linear Regression (LR) and Support Vector Machines (SVM) have produced results that are inefficient and inaccurate, leading to the development of Deep Learning-based solutions (DL). With the increased use of DL techniques in prediction, its performance is showing significant improvements over traditional time series models. With their ability to learn patterns and predict future prices, DL models such as Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are becoming increasingly popular. Even so, these models continue to evolve and are continually revised to make them more efficient and better. To forecast the price for the 11th day, we present an RNN-GRU (Gated Recurrent Unit) model based on four independent features and the prices from the previous ten days. Furthermore, this model's performance was compared to that of an LSTM and CNN model. The results of the last test revealed that our proposed model outperformed the CNN and LSTM models.
Generating Natural Language Adversarial Examples Based on the Approximating Top-K Combination Token Substitution

Deep Neural Networks (DNNs) have been widely used in Natural Language Processing (NLP) applications. However, due to the lack of interpretability, recent studies have shown that the DNN-based models used in NLP are vulnerable to adversarial attacks by adding subtle perturbations into inputs. Among the various existing adversarial attack methods, it is still challenging on how to maintain the high similarity between generated adversarial text and the original text while ensuring both grammatical correctness and semantic preservation. In this paper, we propose a novel attack method based on the approximating Top-K combination token substitution to generate adversarial text. We extend the sequential substitution that is commonly used in the existing methods into a combination substitution, and combine it with Monte Carlo simulation to significantly expand the search space. Furthermore, based on the part-of-speech information, we combine the synonym token substitution strategy and the language model based substitution strategy to generate adversarial texts that are semantically consistent with the original texts. Extensive experiments illustrate that our method outperforms previous methods regarding attack efficiency, perturbation rate, and semantic similarity. Moreover, training on adversarial samples generated by our approach can effectively improve the robustness of the model.
Research on pedestrian trajectory prediction by GAN model based on LSTM

As the application scenarios of unmanned vehicles are gradually diversified and complicated, how to improve the safety and efficiency of unmanned vehicles requires the prediction of dynamic obstacles for mobile robots, and this paper studies a pedestrian trajectory prediction algorithm that can be applied with unmanned vehicles in the first-view video images. A pedestrian trajectory prediction model method based on GAN model of LSTM is proposed. Firstly, in the feature extraction module, the observer camera motion state vector, pedestrian pose information and pedestrian history trajectory are considered as loses. Secondly, in the attention module, the motion attention mechanism is used to measure the degree of influence of the observer camera motion on the pedestrian trajectory, the pose attention mechanism is used to extract hidden features from the human pose, and the interaction attention mechanism is used to model the social interaction between pedestrians, and finally the GAN network module is used to obtain the predicted trajectory using generative adversarial networks. In this paper, we use ETH this dataset for model analysis, and the experimental results show that the proposed model of GAN model based on LSTM can effectively predict the future motion trajectory of pedestrians.
A Study on Personality Prediction & Classification Using Data Mining Algorithms

Personality is an integral part of one's resume and life, using machine learning we can easily predict and classify one personality using intuitive input methods like questionnaire. In this research endeavor we have looked to achieve this very motive. After reviewing past work, we concluded to use logistic regression and classifier to classify people into different personality categories and accurately predict it. With this research, we hope to answer some real-world problems by creating a research prototype in the form of a python (Flask) based webapp. The personality classification is solely dependent on the user's inputs, which are compared to the database's recorded values, which is accomplished by training the model on a training dataset, and then eventually categorizing the user's personality into the appropriate group. This is totally an automated system and we have tried to achieve our goal.
Implementation of Decision Tree with Pruning for Predicting the Outcomes of Myocardial Infarction

Over the last decade, the fast growing area of artificial intelligence and machine learning has made its vast impact in various fields such as healthcare sector, security, market, image processing, etc. Deep learning has also evolved these days for prediction and forecasting as the performance of the machine learning algorithms does not show good results with large amounts of data. Whereas, Deep learning shows better results and performance with large amounts of data but it requires large computational power. Nowadays, computational power is not the limitation, hence deep learning is at its boost. The healthcare sector is using deep learning techniques for human disease classification, detection, or even predictions related to disease. Our research is also based upon the technique which is known as Decision Trees. The objective of our research is to predict the various outcomes of Myocardial Infarction (commonly known as a heart attack) using the Decision Trees but also using one more technique to improve the accuracy of prediction and this technique is known as Pruning. This is a type of Cardiovascular disease which affects the blood vessels as well as the heart. Cardiovascular diseases cause deaths in large numbers globally. Due to Cardiovascular diseases, nearly 17.9 million people died in 2019 in which heart attack and stroke were the main reasons for 85% of deaths. A heart attack which is also known as Myocardial Infarction causes damage to the heart muscles because of the decrease in the flow of blood or when the flow of blood completely stops to the coronary artery of the heart.
Crowdsourcing Mobile Data for A Passive Indoor Positioning System - The MAA Case Study

Crowdsourcing radio signal fingerprints to build a radio map for indoor positioning system is an emerging alternative to conventional labour-costly manual survey. However, existing crowdsourced systems heavily rely on ground-truth location inputs or unrealistic constraints on the contributors, deterring a wider adaption of crowdsourced systems. Our work exploits three generic constraints of mobile data to retrieve the locations of the crowdsourced fingerprints and builds a completely passive indoor positioning system that assumes no manual intervention or unnatural constraints on the contributors. The proposed system was further evaluated in the Museum of Archaeology and Anthropology (MAA) with passively crowd- sourced data contributed by actual visitors while visitors can behave naturally without catering to crowdsourcing. Results show that the proposed system can achieve positioning accuracy comparable to traditional manual survey-based system with essentially no extra manual effort.
Trusted-Committee- Based Secure and Scalable BFT Consensus for Consortium Blockchain

Compared with public blockchain, consortium blockchain is more secure and controllable deployed in an enterprise scenario. Byzantine fault tolerance (BFT) consensus is widely applied in consortium blockchain. Although PBFT is the most classic practical BFT consensus with message complexity O(n2), it still faces some security threats and has low consensus efficiency. To address these issues, we propose a secure and trusted BFT (S2BFT) consensus based on trusted committees. S2BFT generates a trusted anonymous number using trust execution environment (TEE) for each server node and selects committees by pseudo-random algorithm. S2BFT can efficiently reach consensus by the committees with an O(m*n) message complexity. In addition, correctness analysis proves that S2BFT can resist more attacks than traditional BFT consensus and tolerate 1/2 byzantine server nodes. Results further demonstrate the efficiency of the simulated S2BFT implementation.