Miracle-dz/UniFAR-FTUs huggingface dataset:
A three-party attack-defense deception game model based on evolutionary

A three-party evolutionary game model is constructed by combining the cyber deception, the defender (intrusion detection system), and the attacker. The attackers choose attack strategies to gain greater benefits. The cyber deception can induce attackers to attack fake vulnerabilities, so as capture and analyze the attackers' intentions. The defenders use the captured attacker information to adjust their defense strategies and improve detection of attacks. Using cyber deception to enhance the defender choice of strategy, reduce attacker's profit, enable defender to play their own superior strategy, reduce node resource overhead, and prolong network survival time. Through the capture and feature extraction of attacker's attack information, the attack feature database of intrusion detection system is improved, and the detection probability of the attack by the defender is increased. According to the simulation results, the cyber deception can provide the defender with the attacker's attack information in the process of attack and defense, increase the probability of the defender's successful defense, speed up the convergence speed of the optimal defense strategy, and reduce the convergence speed of the attacker's optimal strategy. It is proved that the cyber deception as a third-party participant can effectively help the defender to protect the security of the network.
Optimization of Vehicle Structures based on Improved Deep Forest and Grey Wolf Optimizer

Optimization design of the vehicle thin-walled structures (TWS) is one of the critical ways to reduce structural mass and enhance the mechanical properties of the whole body. Traditional numerical simulations are effective and have been widely used to solve this problem. However, carrying out a high-fidelity simulation is really time-consuming and the optimization design heavily relies on engineer's experience. In this work, a deep regression forest model (DRF) is used to approximately simulate the TWS physical system, and through an improved grey wolf optimizer (AIGWO), the optimization design is adaptively realized. Specifically, a multi-grained circular scanning and pooling-based cascade forest are used to build the DRF model. The mathematical expression of the DRF is generated. Compared with five state-of-the-art machine learning-based models, the DRF shows the best accuracy. Subsequently, a reward strategy is proposed to enable AIGWO to adaptively determine its optimization actions. Three classic TWS vehicle parts are optimized. The results demonstrate that the AIGWO outperforms other four advanced swarm intelligence algorithms. The structural mass of three TWS cases are reduced and the mechanical properties are enhanced. The whole computational time based on the proposed method is highly shortened compared with numerical simulation. In conclusion, the proposed method can efficiently realize the optimization design of vehicle TWS problem.
The First Concept and Real-world Deployment of a GPU-based Thermal Covert Channel: Attack and Countermeasures

Thermal covert channel (TCC) attacks have been studied as a threat to CPU-based systems over recent years. In this paper, we propose a new type of TCC attack that for the first time leverages the Graphics Processing Unit (GPU) of a system to create a stealthy communication channel between two malicious applications. We evaluate our new attack on two different real-world platforms: a GPU-dedicated general computing platform and a GPU-integrated embedded platform. Our results are the first to show that a GPU-based thermal covert channel attack is possible. From our experiments, we obtain a transmission rate of up to 8.75 bps with a very low error rate of less than 2 % for a 12-bit packet size, which is comparable to CPU-based TCCs in the state of the art. Moreover, we show how existing state-of-the-art countermeasures for TCCs need to be extended to tackle the new GPU-based attack at the cost of added overhead. To reduce this overhead, we propose our own DVFS-based countermeasure which mitigates the attack, while causing $2\times$ less performance loss than the state-of-the-art countermeasure on a set of compute-intensive GPU benchmark applications.
Prediction of Heart Disease Based on Machine Learning Algorithms

The objective of this study is to create efficient machine learning (ML) models for the Heart Disease Prediction System (HDPS). This study shows how classification techniques for machine learning can forecast heart illness. To forecast and alert patients about potential cardiac abnormalities, machine learning (ML) models including Logistic Regression (LR), Decision Tree Classifiers (DTC), Random Forest Classifiers (RFC), Support Vector Classifiers (SVC), and voting classifiers are employed. Few challenges were encountered while developing the models, such as underfitting the model without balancing the data with decision tree classifier. The voting ensemble technique overcame the challenges and allowed for a generalized model on balanced data with high accuracy. The purpose of this investigation is to see whether the technique for properly forecasting heart disease is based on health factors. A voting classifier is made up of LR and RFC. Among all models, this voting classifier had the highest accuracy of 98.36%.
Artificial Magnetic Conductor based Composite substrate Antenna for Low Frequency Wireless Underground Sensor Networks

In the current era of Wireless Internet of Things, developing low frequency planar antennas for underground communication is a challenging task. For underground electromagnetic wave propagation, low frequencies in the range of 300 MHz to 900 MHz are optimal. As the frequency drops, the size of the planar antenna grows, making design more difficult. This paper describes a planar antenna with shorting vias and an airAMC composite substrate that operates at 385 MHz and has a peak gain of 1.3 dB. This study lays the groundwork for the development of low frequency planar antennas with positive gain and a comparably compact construction.
A U-net with Gated Recurrent Unit and Efficient Channel Attention Mechanism for Real-time Speech Enhancement

Speech enhancement has many real-world applications, like cochlear implants and hearing aids. We propose a Unet with a gated recurrent unit and an efficient channel attention mechanism for single-channel speech enhancement. ECA can be used to implement a novel cross-channel interaction without reducing its dimensionality. Network performance was significantly improved by using an adaptable kernel size in module testing. Additionally, the U-Net architecture uses gated recurrent units (GRU), which yields a causal system suitable for real-world use. GRU is used for learning long-range dependencies. Speakers and noise types are different for training and testing because it is independent of both. Compared to LSTM-based models, UGRU with ECA consistently leads to better objective comprehensibility and perceptual quality.
Formal Analysis of Timing Diversity for Autonomous Systems

The design of autonomous systems, such as for automated driving and avionics, is challenging due to high performance requirements combined with high criticality. Complex applications demand the full performance of commercial high performance multi-core systems of-the-shelf (COTS), with or without accelerators. While these systems are optimized for performance, hard real-time requirements and deterministic timing behavior are major constraints for safety-critical systems. Unfortunately, infrequent timing outliers caused by interleaved hardware-software effects of COTS systems complicate traditional worst-case design. This conflict often prohibits deploying COTS hardware and consequently prevents sophisticated applications, too. Recently, an approach called Timing Diversity was introduced, which proposes to exploit existing dual modular redundant hardware platforms to mask deadline violations. This paper puts Timing Diversity on a theoretical foundation and provides specification for different implementations. It demonstrates that Timing Diversity needs fast recovery to be effective, proposes a recovery strategy and provides a mathematical model for the reliability of the resulting system. Using experimental data in a Linux based system, it shows that fast recovery is useful, making Timing Diversity a realistic option for compute demanding hard real-time applications.
Automatic and Comprehensive Classification of the Morphological Forms of C. albicans

Symptoms of fungal diseases can be similar to those of COVID-19, and lab tests are necessary to determine the precise infections a person is suffering from. Candida albicans (C. albicans) is an opportunistic pathogenic yeast that causes nosocomial infections, and patients exposed to hospital environments are vulnerable. The presence and number of its morphological forms (morphotypes/phenotypes) i.e., ellipsoidal/ovoid cells, budding cells, and hyphae - are useful for detecting the pathogenic propensity of C. albicans. However, there are certain challenges: fatigue and human errors associated with manual screening. In this paper, we propose a novel, automatic method of detecting and counting all of the morphological phenotypes of C. albicans from simple microscope-images of stained smears. The method involves color-segmentation in the HSV space using RUS-Boosted Decision Trees, and classification based on features such as area, width, the presence of constriction, and aspect ratio. The method was tested on 11 images with the training set formed from 43 images. The method showed a specificity of 79.3, 98.1, 100 & 98.6, sensitivity of 92.8, 82.2, 100 & 88.4, and accuracy of 87.1, 94.7, 100 & 96, on ellipsoidal yeast cells, budding yeast cells, hyphae and cluster of cells, respectively.
Liveness-Aware Checkpointing of Arrays for Efficient Intermittent Computing

Intermittent computing enables computing under environments that may experience frequent and unpredictable power failures, such as energy harvesting systems. It relies on checkpointing to preserve computing progress between power cycles, which often incurs significant overhead due to energy-expensive writes to Non-Volatile Memory (NVM). In this paper, we present LACT (Liveness-Aware CheckpoinTing), as an approach to reducing the size of checkpointed data by exploiting the liveness of memory objects: excluding dead memory objects from checkpointing does not affect the correctness of the program. Especially, LACT can analyze the liveness of arrays, which take up most of the memory space but are not analyzable by existing methods for detecting the liveness of scalar objects. Using the liveness information of arrays, LACT determines the minimized checkpoint range for the arrays at compile time without any runtime addition. Our evaluation shows that LACT achieves an additional reduction of checkpointed data size of 37.8% on average over the existing state-of-the-art technique. Also, our experiments on a real energy harvesting environment show that LACT can reduce the execution time of applications by 27.7% on average.
Chaos-based secured modified strong recovery conditions for least support orthogonal matching pursuit in the noisy case

In this article, our previous work on the Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP) reconstruction algorithm for noisy measurements is secured by utilizing the Logistic chaotic map [1]. Additionally, storage space and transmission bandwidth can be saved by transmitting the key parameters to the receiver instead of transmitting the whole measuring matrix–the initial condition and control parameter of the Logistic map act as a secret key (password). Reconstructed signal-to-noise ratio, key sensitivity, and key space tests are performed to check the recovering performances and security of chaos-based LSD-OMP.
Strategic Decisions using Machine Learning with Interpretative Structural Modelling (ISM) on Digital Platform Data for Marketing Intelligence

Systematized strategic decisions using marketing intelligence gathered through data mining and machine learning using open-source digital platforms are the focal theme of this research. The parameters that affect product & service marketing are considered here with their interlinkages in making effective, sustainable strategic decisions, thus aiding in expounding the model's dimensions for market intelligence and analysis. This article is a conceptual, descriptive research study in which the investigative technique used in this research is conducted based on expert opinions to derive a model using Interpretive Structural Modeling. The data from digital platforms using machine learning can be converted into marketing intelligence linked to various marketing factors for their interrelations model derived through Interpretative Structural Modelling (ISM) technique into well-defined and structured models for making effective, strategic decisions. This research study describes the dimensions linked with a specific product outcome as an illustration of a mobile phone model for specific linkages strengths for strategic decisions. A few studies focused on leveraging data from digital platforms have different approaches to gathering marketing intelligence for digital platforms, significantly linking it with attributes for strategic decision-making. This study uses ISM for specific product sentiments and marketing factors to derive a model for effective strategic decisions.
Deep Learning Model for Detection and Recognition of Fire based on Virtual Reality Video Images

Fire detection and recognition is an important aspect of fire safety, and the use of virtual reality video images and deep learning (DL) methods can help to optimize this process. Deep learning (DL) is the sub-field of machine learning (ML) which utilizes the artificial neural networks (ANN) to train and analyze predictions. These networks are more suitable for processing enormous amounts of data which is better for image recognition. Based on the fire status and immersive view, the detection and recognition of fire are detected. Deep learning algorithms can be trained using these images to recognize patterns and identify fires, smoke, and other indicators of fire. This paper introduced the new fire detection model which detects the fire from video footage and also images collected various online sources. The proposed model used the pre-trained model RESNET-50 to train the fire affected videos. To detect the fire affected region the feature extraction method Histogram of Oriented Gradients and Radial Basis Function Networks (RBFNs) used to detect the fire affected images.
Binary ReRAM-based BNN first-layer implementation

The deployment of Edge AI requires energy-efficient hardware with a minimal memory footprint to achieve optimal performance. One approach to meet this challenge is the use of Binary Neural Networks (BNNs) based on non-volatile in-memory computing (IMC). In recent years, elegant ReRAM-based IMC solutions for BNNs have been developed, but they do not extend to the first layer of a BNN, which typically requires non-binary activations. In this paper, we propose a modified first layer architecture for BNNs that uses k-bit input images broken down into k binary input images with associated fully binary convolution layers and an accumulation layer with fixed weights of $2^{-1}, \ldots, 2^{-k}$. To further increase energy efficiency, we also propose reducing the number of operations by truncating 8-bit RGB pixel code to the 4 most significant bits (MSB). Our proposed architecture only reduces network accuracy by 0.28% on the CIFAR-10 task compared to a BNN baseline. Additionally, we propose a cost-effective solution to implement the weighted accumulation using successive charge sharing operations on an existing ReRAM-based IMC solution. This solution is validated through functional electrical simulations.
TPP: Accelerate Application Launch via Two-Phase Prefetching on Smartphone

The fast app launch is crucial to users' experience and it is one of the eternal pursuits of manufacturers. Page fault is a critical factor leading to long app launch latency. Prefetching is the current method of reducing page faults during app launch. Before app launch, prefetching all demanded pages of the target app can speed up the app launch effectively, but it always uses the memory of several hundred MB, leading to low memory and slowing other apps' launch. Prefetching during application launch uses memory effectively, however, current methods are not aware of the order of pages accessed, causing noticeable accessing-prefetching order inversions, which results in limited acceleration of app launch. In order to accelerate the application launch effectively with little memory usage, we propose a Two-Phase Prefetching schema (TPP), which performs prefetching via two phases: 1) Before the app launch, to increase the efficiency of memory usage in prefetching, TPP prefetches few critical pages with app prediction, which is based on Long Short-Term Memory (LSTM) with high accuracy. 2) During app launch, TPP prefetches the rest of the critical pages via an order-aware sliding window method, resolving the accessing-prefetching order inversions and significantly reducing the app launch latency. We evaluate TPP on Google Pixel 3, compared to the state-of-the-art method, TPP reduces the application launch time by up to 52.5%, and 37% on average, and the data prefetched before the target application started is only 1.31 MB on average.
Predicting the academic performance of students with GPcSAGE

Educational data mining is a popular research area in data mining, and predicting student performance is one of the important research topics in educational data mining. In order to predict student performance in a timely and accurate manner, this paper proposes a Graph Pearson correlation Sample and AggreGatE (GPcSAGE) model based on graph neural networks. The sampling probability of neighboring nodes similar to the target node is optimized to weaken the influence of abnormal target node attributes on the prediction results and reduce the sampling variance. The algorithm efficiency and prediction accuracy are improved by reconfiguring the aggregation function to aggregate more important information. The experiments demonstrate the effectiveness of the method, which helps to predict students' learning trends and effects for precise teaching interventions to improve teaching quality.
Mangoes Taste Detection System Using Deep Learning

Machines have evolved significantly in recent years due to incorporating new technology. As a result, advanced machine learning and image processing have become hot issues for scholars. This study provides a deep learning-based enhanced approach for detecting mango tastes (sweet and sour) from mango photos. A total of 3132 mango image data were used for training and validation. This system employs a modified YOLO v3 algorithm. On sour mango data, this system received 0.84 for average precision (AP), 0.75 for precision, 0.94 for recall (true positive rate), and 0.83 for F1-score (combination of precision and recall), whereas, on sweet mango data, this system received 0.97 for AP, 0.98 for precision, 0.97 for recall, and 0.98 for F1-score.
A Microstrip Strip Line Based High Dynamic range Optical Front-end for LiDAR Application

This work presents a 180 nm CMOS based transimpedance amplifier (TIA) for time-of-flight (ToF) LiDAR. The proposed design includes a T-shaped microstrip line (MSL) network as first phase, Variable Gain Common Gate (VGCG) Transimpedance as second phase, and a post amplification as final stage. The MSL section reduce the impact of the photodiode capacitance achieving higher bandwidth performance, while the VGCG provides low input impedance and performs current to voltage conversion with high dynamic range (DR). A post amplification stage is integrated at the output of VGCG TIA in order to achieve high gain. The work demonstrates a wide band of 1250 MHz, a maximum transimpedance gain, high DR and an ultra-low noise of 91.42 dB$\Omega$, 71dB, and 3.71 pA/sqrt (Hz). The TIA along with post amplification consumes a power of 33 mW.
Two-Stream Neural Network for Post-Layout Waveform Prediction

The gap between pre- and post-simulation, as well as the considerable layout time, increases the significance of the post-layout waveform prediction in dynamic random access memory (DRAM) design. This study develops a post-layout prediction model using the following two-stream neural network: (1) a multi-layer perceptron neural network to calculate the coupling noise by using the physical properties of global interconnects, and (2) a convolutional neural network to compute the time series trends of the waveforms by referencing adjacent signals. The proposed model trains two types of heterogeneous data such that accuracy of 95.5% is achieved on the 1b DRAM process 16Gb DDR5 composed of hundreds of millions of transistors. The model significantly improves the design completeness by pre-detecting the deterioration in the signal quality via post-layout waveform prediction. Generally, although a few weeks are required to obtain post-layout waveforms after the circuit design process, waveforms can be instantly predicted using our proposed model.
Framework for Voice-controlled AI Teaching Assistant for Specially-abled

Recent advancements in technology have triggered a revolution in the education sector. One of the significant developments of this change is being granted access to vast online resources from various world class institutes. The motivation behind this paper is to be able to create a technological solution for providing equal opportunities to specially-abled online learners who may have physical disabilities such as visual impairment, loss of hands/weak hand motor skills, etc., and enable their participation for a more inclusive society. The paper uses a twofold approach. Firstly, it provides literature on various state-of-art assistive technologies for e-learning portals. Secondly, the paper proposes a learning framework as teaching assistant for specially-abled, which is artificial intelligence based and interactive voice enabled. The proposed framework has three modules completely controlled by voice commands for self-paced -reading, learning and auto-marking assessment. The assessment module generates fill-in-the-blanks and question-answers, which is a part of continuous comprehensive tests. As a case study, the proposed framework is prototyped on an e-learning platform DIKSHA, which hosts digital books from National Council of Educational Research and Training. Natural language processing toolkit based on Python libraries has been used. The results have been found encouraging. Extensions for future work are also discussed.
Applications of electronic nose and machine learning models in vegetables quality assessment: A review

Vegetables are an integral part of a balanced diet. They are good source of nutrition enriched with vitamins, mineral and antioxidants. They are prone to spoilage after harvesting without proper storage. Their quality can be determined by chemical analysis, high-performance liquid chromatography and near-infrared detection method. These methods are time taking, required a high cost equipment and trained person to perform. This article discussed the basic concept of electronic nose and machine learning. Electronic nose is used to detect gases excreted from vegetables and produced electronic signals. Machine learning is trained on these signals and predict the quality of vegetables. This paper presents the application of an electronic nose system with machine learning models. It is studied that this method is a cost-effective, portable and potential upcoming technique to overcome quality issues in vegetables.