Miracle-dz/UniFAR-FTUs huggingface dataset:
Half-Farmer: A Human-Machine Augmented Learning Framework for Seed Germination Recognition in Smart Farming

Smart farming technologies have the potential to facilitate low-cost crop monitoring for increasing crop yields. This work proposes a human-machine augmented learning framework for smart farming, called Half-Farmer, which effectively recognizes the germination stages of wheat seeds. Human experts (i.e., agriculturalists) and a recognition model based on convolutional neural networks (CNN) collaboratively label germination stages of seeds on images according to the unique characteristics of different germination stages. Initially, a CNN-based recognition model augments weak recognition results on images for human experts to cross-validate the germination stages. With the cross-validated labels, the initial recognition model is gradually updated with a greater understanding of seed germination characteristics. The iterative human-machine collaborative process facilitates model evolution in Half-Farmer. Therefore, human experts further jointly make decisions together with the evolved recognition model to resolve the ambiguity in germination stages arising from either human subjective opinions or incorrect recognition by early weaker models. Extensive experimental results indicate that the Half-Farmer achieves an overall Fl-score of 79.5%, an overall recall of 78.4%, and an overall precision of 80.2 %.
Collaborative Edge Computing for Vehicular Applications Modeled by General Task Graphs

Edge computing allows road-side units (RSUs) to assist vehicles for computation. However, for an emerging vehicular application that consists of some mutually dependent sub-tasks, one RSU that has an edge server can’t afford such workload. Thereout, we propose a scheme: every vehicle currently selects an appropriate RSU to offload its application; then, the application is computed via collaboration of RSUs; once it is finished, the result is delivered back to the vehicle. After that, to reduce latency and increase reliability, we formulate a joint optimization problem that determines the details of offloading, computation, and result delivery. For finding a solution to this non-convex problem, we transform it into a Markov Decision Process (MDP) and address it through multi-agent deep deterministic policy gradient (MADDPG). By numerical evaluation, we show that the proposed scheme with MADDPG can significantly lower latency and enhance reliability in time-varying vehicular networks.
A New Targeted Online Password Guessing Algorithm Based on Old Password

Password authentication is a widely used identity authentication method for computer supported cooperative systems. However, the frequent occurrence of password leakage incidents has become a universal problem, and the leaked passwords seriously threaten the security of users’ unleaked passwords. In order to gain a deeper understanding of the relationship between users’ old passwords and new passwords and help users choose a securer new password when their old passwords are leaked, we propose a new targeted online guessing algorithm, Targuess-II+, based on old password in this article. As a new probabilistic algorithm, Targuess-II+ not only supports the application of strong transformation rules at any positions in a password, but also shows the transformation process from one password to another. Our analysis and experimental results have demonstrated that Targuess-II+ obtains better performance in terms of crack rate and efficiency compared with other existing algorithms.
Decentralized Federated Learning for Intrusion Detection in IoT-based Systems: A Review

Internet of Things-based systems are typically distributed systems and thus inherit all issues related to the need to guarantee confidentiality, integrity, and availability. Moreover, IoT-based systems are vulnerable to several attacks, mainly due to the weakness of IoT devices, which have little computational and memory power, necessary for more sophisticated security features. To build robust infrastructures, one of the traditional strategies to deal with these problems involves intrusion detection and prevention techniques. Implementing them in a centralized way is usual, which leads to not being scalable for IoT systems with an increasing number of devices. Moreover, it implies an unacceptable single point of failure. Besides, sending all collected data to a centralized server in the cloud poses a significant risk to the privacy of information. Recently, machine learning techniques, as decentralized federated learning, combined with distributed ledger technologies, have been used to implement more robust and privacy-preserving intrusion detection systems for IoT-based systems. However most current decentralized federated learning approaches depend on a centralized server, and so have a single point of failure. The centralized server is used to aggregate the local trained models obtained by means of deep learning algorithms performed on edge and fog devices. This paper reviews state of the art on intrusion detection based on totally decentralized federated learning and distributed ledger techniques applied to minimize IoT security threats, identifies open problems, and recommends future research directions to cope with them.
Evaluating Machine LearningWorkloads on Memory-Centric Computing Systems

Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (CPU, GPU) waste large amounts of energy and execution cycles due to the data movement between memory units and processing units. Memory-centric computing systems, i.e., systems with processing-in-memory (PIM) capabilities, can alleviate this data movement bottleneck. Our goal is to understand the potential of general-purpose PIM architectures to accelerate ML training. To do so, we (1) implement several classic ML algorithms (namely, linear regression, logistic regression, decision tree, K-Means clustering) on a real-world generalpurpose PIM architecture, (2) evaluate and characterize them in terms of accuracy, performance and scaling, and (3) compare to their counterpart state-of-the-art implementations on CPU and GPU. Our evaluation on a real memory-centric computing system with more than 2500 PIM cores shows that PIM greatly accelerates memorybound ML workloads, when the necessary operations and datatypes are natively supported by PIM hardware. For example, our PIM implementation of decision tree is 27× faster than the CPU implementation on an 8-core Intel Xeon, and 1.34× faster than the GPU implementation on an NVIDIA A100. Our PIM implementation of K-Means clustering is 2.8× and 3.2× faster than CPU and GPU implementations, respectively. We provide several key observations, takeaways, and recommendations for users of ML workloads, programmers of PIM architectures, and hardware designers and architects of future memory-centric computing systems. We open-source all our code and datasets at https://github.com/CMU-SA FARI/pim-ml.
Automatic Modulation Classification for Adaptive OFDM Systems Using Convolutional Neural Networks With Residual Learning

Automatic modulation classification (AMC) is becoming a promising technique for future adaptive wireless transceiver systems. The existing blind modulation classification (BMC) methods for orthogonal frequency division multiplexing (OFDM) fail to achieve the required performance by using statistical-based methods. Thus, the modulation classification research community is trying to adopt the deep learning (DL) method to improve the modulation classification accuracy. However, most of the existing DL methods for AMC of OFDM that involve the extraction of statistical features from the signal do not work for adaptive transceiver systems where the signal parameters are changed dynamically. In this paper, we design and implement AMC for adaptive OFDM systems by using a convolutional neural network (CNN) with residual learning. The proposed AMC can identify the modulation format of the received OFDM signal with different number of subcarriers, randomized carrier frequency offset (CFO), symbol timing offset (STO), phase offset, and unknown channel state information. We use residual learning to mitigate the effect of varying CFO, STO, and AWGN noise on the received OFDM signal. A larger pool of modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK, $\pi $ /4-QPSK, minimum shift keying, 8-PSK, 16-quadrature amplitude modulation (QAM), and 64-QAM are being considered for the proposed AMC for OFDM system in a dynamic environment. The performance and complexity of the proposed AMC are compared with the existing statistical feature-based and DL-based approaches. The proposed AMC for the OFDM system is also verified on the real-time data set generated from the universal software radio peripheral testbed setup.
A Fault-tolerant and Cost-efficient Workflow Scheduling Approach Based on Deep Reinforcement Learning for IT Operation and Maintenance

With the promotion of cloud computing, a large number of hardware and software systems in the cloud bring massive and complex operation and maintenance (O&M) work. To ensure the O&M efficiency of IT infrastructures, it is necessary to implement automatic and reliable scheduling for the directed acyclic graph (DAG) workflow which is composed of multiple O&M tasks. Considering the changing status of networks and machines in the cloud and the position constraints that some tasks must be executed on the specified machines in some O&M scenarios, we propose a novel workflow scheduling approach based on Deep Reinforcement Learning (DRL) to minimize the workflow execution makespan and implement the fault tolerance with the position constraints of tasks execution. In our proposal, we first design a fault-tolerant mechanism according to the reliability requirement and the probability distributions of the machine failure parameters with consideration of different failure rates in the heterogeneous environment. Then, we employ proximal policy optimization (PPO) to optimize the task scheduling strategy and ensure the strategy to satisfy the position constraints of tasks execution by action masking in proximal policy optimization. The experimental results show that our proposal can effectively reduce the makespan of the fault-tolerant workflow on the premise of 99.9% reliability.
Investigations on the relative intensity noise of a two-state quantum dot laser

We investigate the spectrally-resolved relative intensity noise (RIN) of a dual state emitting quantum-dot (QD) laser in dependence on the laser biasing conditions. We study the RIN under free-running conditions as well as under external optical feedback (OFB). We ï¬nd an improvement in RIN of the free-running laser when ground-state (GS) and excited-state (ES) emit simultaneously as compared to a single-state emission. Furthermore, we ï¬nd an improvement in RIN under external OFB.
Study on Elliptic Partial Differential Equations

The thesis entitled with Study on Elliptic Partial Differential Equations is a serious and a keen study towards the ambit of most influential and practical scenario of Mathematics i.e. Partial Differential Equations. Before in-sighting towards PDE, this thesis includes two chapters dedicated to partial differential equations. Chapter 1 discusses the classification of partial differential equations providing its detailed classification and definitions (see 1.1.1) and also, insight classification of partial differential equations is also included in B. Chapter 1 is an introductory step towards chapter 2 which provides the basic knowledge of symbols and notations that are immensely used in chapter 2. Chapter 1 keeps its concerns with Quasi-Linear Partial Differential Equations and Semi-Linear Partial Differential Equations. Chapter 2 has a serious agenda of this thesis. Moving further in chapter 2, the governing definition of elliptic partial differential equations is provided (see 2.1.1) which is all the way important in the course of thesis. The section 2.1 provides the outline of all those aspects which have to analysed in section 2.2 which is, thus very important. Chapter 2 begins with the Reisz- Representation Theorem. This theorem establishes an important connection between a Hilbert space and its (continuous) dual space. Moving on, this thesis presents the Bilinear Forms in Elliptic Partial Differential Equations. The second last topic is Poinc˜re Inequality which helps in estimating the norm of a function in terms of a norm of its derivative. The Lax-Milgram Theorem incorporates as more general form of Reisz-Representation Theorem, as it applies to bilinear forms that are not necessarily symmetric.
Neurodynamics Adaptive Reward and Action for Hand-to-Eye Calibration With Deep Reinforcement Learning

Calibration performed by a robotic manipulator is crucial in the field of industrial intelligent production, as it ensures precise and accurate measurements. In this paper, we present a new method for addressing the hand-to-eye calibration problem using deep reinforcement learning. Our proposed algorithm utilizes an actor-critic framework and incorporates neurodynamics adaptive reward and action functions, which allows for better convergence, reduces the dependence on the initial value, and overcomes the local convergence issues of traditional deep reinforcement learning method. Additionally, we introduce a step-wise mechanism under the guidance of the attention mechanism, and zero stability to handle the complexity of the calibration task in challenging environments. A number of experiments were conducted to demonstrate the validity of the proposed algorithm. The experimental results show that our proposed algorithm can achieve a nearly 100% success rate after training phase. Additionally, we compared our proposed algorithm with other widely used methods, such as deterministic deep policy gradient (DDPG) and soft actor-critic (SAC) to further demonstrate its effectiveness.
A Second Minimum Approximation Method for Layered Min-Sum Decoding of QC-LDPC Codes

Low-density parity-check (LDPC) codes have attracted tremendous attention for their excellent error-correction performance and inherent fitness for high-parallelism implementation. With great complexity reduction and friendly for hardware implementation, the min-sum (MS) decoding algorithm for LDPC codes gains widespread applications in practical. In the MS decoding algorithm, finding the first two minima among input variable-to-check messages is the most complex part. To further reduce the complexity of the MS decoding algorithm, an efficient single-minimum (single-min) based layered MS decoding for quasi-cyclic LDPC (QC-LDPC) codes is proposed in this paper. It is found that statistically the difference between the first minimum and second minimum increases as the decoding becomes more convergent. Accordingly, in the proposed single-min scheme, the second minimum is approximately derived based on the first minimum and an index indicating the convergence degree. Besides, some other metrics that are easily obtained are used to refine the second minimum. Simulation results show that the proposed method can significantly improve the error-correction performance and lower the average number of iterations, compared with the state-of-the-art single-min based MS decoding algorithms.
Stabilization of Delayed Memristive Neural Networks Driven by Mixed Deception Attacks

This article investigates the stabilization problem of delayed memristive neural networks (DMNNs) driven by mixed deception attacks. It is noted that information is extremely vulnerable to deception attacks when it is transmitted in open network channels, this paper considers the situation of mixed deception attacks on network channels, which will have an effect on both state signals and control signals, and lead to a decline in the control accuracy, as well as affect the performance of actuator. In addition, it will cause system instability. This study proposes a control method that can successfully defend against deception attacks and guarantee system stability. By constructing Lyapunov functional and implementing proposed controllers, the stabilization criteria are derived for MNNs. Finally, the viability of our scheme is demonstrated with a numerical example.
A Floating Calculation Revamp For the Ethereum Blockchain-Based IoT Systems

It has become an industry consensus to integrate blockchain with other technologies to create a complete solution. Among the combinations, the Internet of Things can collect data on a large scale at a low cost, and the blockchain can trust data on a large scale at a low cost. With the development of Internet of Things(IoT) technology, the trend of networking of furniture is more apparent. The following security issues have also received more and more attention. Blockchain technology and smart contracts are widely regarded as a solution that can solve the networking of large scale terminals. But at the same time, due to the features of the blockchain, the floating calculation of smart contracts is unavoidable. This paper proposes a calculation revamp for the Ethereum-based smart contracts. Firstly, we verified the behaviour of our smart contract on Remix. Secondly, we implemented the smart contract on a blockchain-based IoT system. We confirmed the availability and reliability of our revamped smart contract.
Continuous User Trust Assessment Based on Emphasized Contextual Differentiation Behavior Analysis

Masquerade attacks are one of the most dangerous threats in the cloud environment. Attackers masquerade as legitimate users obtaining access to illegally use cloud resources. If attackers masquerades as internal administrator with top-level privileges, they can change security policies or convey confidential information, causing irreparable damage to the system. Building trust on the user side is an important auxiliary to protect cloud resources. Most user trust evaluation research mainly extracts user behavior features to train basic machine learning models, which cannot accurately track abnormal user behavior in real time. In this paper, we propose a continuous user trust assessment scheme as an additional security layer to enhance secure access to cloud resources, which can effectively fuse behavior and contextual information to automatically detect user anomalies and serve as a criterion to assess user trust status. We test on an open-source Windows security log dataset. The experiments show that our approach continuously assesses user trust with good detection performance and alerts on time.
Time Generative adversarial network for the generation of electricity load data

Predicting power electricity consumption is essential for managing the power grid and ensuring a stable and reliable power supply at short and medium terms. Deep learning models are often used for forecasting, but they require a large amount of data to train and acquire power of generalisation. Data availability in real-world scenarios can be limited, as a result, prediction accuracy can be affected in a negative way. In order to overcome this limitation, generative models such as GAN can generate synthetic data in order to supplement the available data and help the model train on data quality data sets both in terms of quality and quantity. Time GAN, a state-of-the-art model for handling time series data, is proposed in this paper to generate a synthetic data-set for Algerian electricity load data. Subsequently, the obtained results show that using synthetic data improves the predictive accuracy of the forecasting model accuracy by 7.45 using different metrics for evaluation.
Atomic layer etching of molybdenum with fluorination and ion bombardment

Atomic layer etching (ALE) process for Mo was developed with surface fluorination using CHF<inf>3</inf> or C<inf>4</inf>F<inf>8</inf> plasmas and ion bombardment using Ar plasma. The Mo surface was fluorinated with fluorocarbon generated by CHF<inf>3</inf> or C<inf>4</inf>F<inf>8</inf> plasmas, and fluorinated Mo was etched by Ar plasma. The C<inf>4</inf>F<inf>8</inf> plasma produced fluorine-rich fluorocarbon layer compared to the CHF<inf>3</inf> plasma and etch per cycle (EPC) of Mo was about 3 times higher in C<inf>4</inf>F<inf>8</inf> plasma than in CHF<inf>3</inf> plasma. EPC of Mo was investigated by changing the bias voltage of Ar plasma, and ALE window was confirmed in the range of 100 ~ 225 V. The EPC of Mo in the ALE window was determined to be 0.8 nm/cycle for CHF<inf>3</inf> plasma and 2.8 nm/cycle for C<inf>4</inf>F<inf>8</inf> plasma, and it was self-limited with increasing Ar plasma time. Fluorine-rich fluorocarbon layer on the Mo surface increases the EPC of Mo.
On Privacy-Preserving Task Assignment for Heterogeneous Users in Mobile Crowdsensing

Task assignment is a key challenge in mobile crowd-sensing because of the varying capabilities of crowd users. Location-based task assignment schemes require users to upload their location to an untrusted platform, which raises many privacy concerns. However, stronger privacy preservation may lead to lower system utility. It is challenging to maximize system utility under privacy preservation for users. In this paper, we propose a privacy-preserving task assignment for heterogeneous users (PTAH) problem in mobile crowdsensing. Specifically, we divide users into two groups: private users with location privacy requirements and public users without location privacy requirements. We first design a privacy-preserving mechanism to obfuscate the actual location of private users. Then we construct a relationship graph based on the locations between users and tasks. We prove that the PTAH problem is an NP-hard problem, so to maximize the system utility, we propose an approximation algorithm based on the greedy algorithm. Then we propose a multi-thread cooperative simulated annealing algorithm to search for a better approximate solution. Finally, we conducted simulations based on the widely-used real-world Roma dataset. The results show that our proposed algorithm consistently outperforms other baseline algorithms.
Creativity Support in AI Co-creative Tools: Current Research, Challenges and Opportunities

Artificial Intelligence technology-driven Creativity Support Tools (AI-CSTs) provide specific field capability support for human creative activities. In this paper, we compare and analyze the current situation and trend of AI-CSTs design space in four aspects: creative stage, support form, support technology, and role diversity. Through a coding study and comparative analysis of 50 AI-CSTs cases, we discuss the impact of AI-CSTs on traditional workflows, the boundaries of AI-CSTs as co-creators, and how to treat AI errors, which provides insights for future AI-CSTs design. We summarize the collaboration framework in AI-CSTs. Finally, this paper also studies the information technology requirements and challenges of AI-CSTs research, which provides a new perspective to understanding the landscape of AI-CSTs.
Improving the non-urgent sanitary transportation

This article introduces the round-trip dial-a-ride problem (RT-DARP). In this variant of the dial-a-ride problem (DARP), each user has a round-trip demand with one morning request and one afternoon request. The novelty of this approach is that a user's maximum ride time is shared between the two requests, creating a dependency between the morning routes and the afternoon routes. The RT-DARP is solved using the small and large neighbour-hood search (SLNS) metaheuristic and further speed up by an improved feasibility testing procedure. Results from this new formulation are compared with results from solving separately the morning and afternoon DARP. The experiments are performed on a new benchmark generated for the RT-DARP. The results from the RT-DARP always outperform the results from the separated formulation. From a managerial perspective, the RT-DARP makes it possible to improve the quality of service without changing the transportation cost.