Miracle-dz/UniFAR-FTUs huggingface dataset:
Link Between Real-Time Scheduling and Time-Triggered Networks

We demonstrate that, for periodic systems with offsets (tasks or flows of messages within a time-triggered network), the simulation cycle can be confined to the range of [0, hyperperiod) only under the condition that an idle point exists at the hyper-period. Furthermore, we establish that ensuring both (1) contention-freedom and (2) that no offset exceeds the value of the period minus duration, is a sufficient condition to guarantee the presence of an idle point at the hyper-period. Most contemporary methods aiming to eliminate latency are implicitly based on these properties and fail to propose a schedule if the input system does not allow a contention-free solution. Consequently, we propose a heuristic approach to scheduling periodic flows of frames within time-triggered networks. Our method focuses on minimizing latency, without necessitating a solution where every frame at every output port is contention-free, and it effectively manages the cyclicity problem.
Diagnosis of lung cancer based on CT scans using Vision Transformers

Lung cancer remains a major global health problem that requires early and accurate diagnosis to improve patient outcomes. Assessment by specialists is time-consuming, tedious and leads to diagnostic inconsistencies. This has led to the development of computer-aided diagnosis systems for lung cancer diagnosis. Traditionally, proposed systems have used a transfer learning approach with CNN-based models. It has also been observed that cross-validation is usually not applied in these studies. On the other hand, cross-validation contributes to more reliable results by enabling the developed models to generalise to various image samples. In recent years, Vision Transformer (ViT) models have shown remarkable success in various computer vision tasks, including image classification. In this paper, we present our pioneering approach to lung cancer classification using various Vision Transformer-based models. However, we recognised the potential of these models to capture fine-grained patterns in medical images. To ensure the robustness and reliability of our models, we also introduced 5-fold cross-validation as a key component of our methodology. The proposed system yielded the highest results with 99.69% accuracy, 99.62% precision and 98.80% recall. This research represents an important step in utilising the latest computer vision techniques to improve lung cancer diagnosis, ultimately contributing to better patient care and outcomes.
A hybrid-Lp-Norms-Oriented Robot Kinematic Calibration Model Optimized by a Novel Newton Interpolated Differential Evolution Algorithm

Kinematic calibration can obtain real kinematic parameters by the establishment and the solution of the kinematic error models, achieving high-precision control of an industrial robot. However, the data collection points frequently fail to cover the entire robot workspace in data collection due to human error, thereby yielding incomplete robot kinematic calibration (RKC) data. Moreover, existing RKC models all adopt a single distance-oriented Loss scheme, resulting in the ignoration of the RKC data’s characteristics demonstrated by other metrics. Motivated by these discoveries, this work first fills in the RKC data as a preprocessing step for robot calibration. Then, we propose a hybrid-Lp-norms-oriented RKC (HLRKC) model to address the RKC data’s characteristics issue. It adopts twofold ideas: a) aggregating multiple Lp norm effects to form a hybrid Loss; b) adaptively adjusting the weights of all Lp norms in the hybrid Loss. Subsequently, based on the HLRKC model, we present a new Newton Interpolated Differential Evolution (NIDE) algorithm to form a NIDE-HLRKC calibrator. Experimental results demonstrate that the HLRKC-NIDE calibrator substantially exceeds the state-of-the-art calibrators in calibration accuracy and convergence rate. Hence, it strongly supports its excellent potential in robot calibration issues.
Design and Implementation of Fire Alarm for Big City Based on Cloud Computing System

In today world, smart cities with smart infrastructure are a fast-growing subject of study. Fires can occur in open regions, agricultural land, woods, offices, residences, and other places, resulting in severe resource loss. This proposed research demonstrates how to improve low-cost industrial and household safety systems by combining innovative design methodologies and enhancing (WSN) capabilities. Different types of sensors were employed in this investigation to ensure and offer precise solutions to the system. Cloud gateway is used for transferring the collected sensors information Smoke, humidity and temperature sensors are deployed for collection of environmental parameters. This wireless home safety system may be used by anybody due to the basic hardware circuit design. The means of informing users about the system was through a notification. From the sensors, the smartphone receives gas, flame, temperature, and humidity information. It will provide the coordinates of the fire when it detects that the data collected with control levels exceeds a predetermined threshold and uses Remote xy and a Telegram Bot to alert the home's owner and neighbors through a mobile device.
Comparative Analysis of Traditional and Contextual Embedding for Bangla Sarcasm Detection in Natural Language Processing

Sarcasm, a sort of sentiment characterized by a disparity between the apparent and intended meanings of the text, is a key component of sentiment analysis, opinion extraction, and social media analytics. However, sarcasm detection in Bangla has not received sufficient research attention yet. Moreover, there hasn’t been a significant amount of study done comparing traditional and contextual word embeddings for the Bengali language. This study aims to address this gap by comparing traditional embedding by using the Bidirectional Gated Recurrent Unit - BiGRU model and contextual embedding by using Bidirectional Encoder Representations from Transformers - BERT for sarcasm detection in Bangla. The collection of the dataset of Bangla text was from social media platforms, containing labelled instances - whether it provides sarcasm or non-sarcasm. Pre-trained word embeddings i.e. GloVe and FastText are used as traditional embedding for this study. By using metrics like precision, recall and F1-score, the performances for both models have been obtained. When the two traditional word embedding approaches are compared, GloVe embedding with Bi-GRU has outperformed FastText embedding with a macro-averaged F1 score of 0.9395. On the other hand, contextual word embedding using BERT has outperformed both the traditional approaches having a better macro-averaged F1 score of 0.9572 and greater class-wise performance as compared with traditional embedding for both non-sarcastic (96%) and sarcastic (96%) text detection. In our findings, contextual word embedding i.e. BERT has performed better as compared with the two traditional word embeddings for this specific Bangla sarcasm detection binary classification task.
CNN-BiGRU Speech Emotion Recognition Based on Attention Mechanism

The speech emotion recognition system can significantly improve the efficiency of human-computer interaction by accurately recognizing emotional information in speech. This system typically includes two main steps: speech feature extraction and emotion classification. In order to improve accuracy, this article uses MFCC features, short-term energy features, and short-term average zero crossing rate as model inputs, and introduces a convolutional neural network based on attention mechanism and a bidirectional gated loop unit (BiGRU). This method can effectively focus on useful information in speech features. Compared to the CNN-BiLSTM network based on attention mechanism and the CNN-GRU network based on attention mechanism, this method can effectively improve the accuracy of speech emotion recognition when conducting experiments on the Chinese sentiment corpus CASIA.
Empowering Digital Twin: Early Action Decision through GAN-Enhanced Predictive Frame Synthesis for Autonomous Vehicles

Safety concerns surrounding autonomous vehicles (AVs) present significant barriers to their widespread adoption. In AVs, it is necessary to make speedy decisions to safely roam through complex dynamic environments. Interestingly, predicting the future environment can aid in making these early decisions from the partially observed data. This proactive approach is becoming increasingly vital as the number of vehicles on the roads continues to rise, necessitating advanced development strategies for AVs. In this scenario, simulations based on Digital Twin (DT) are proving to be effective in necessary computation during the development and inference phase of the AVs. In fact, the DT system can play a crucial role in making early decisions. Thus, in this research, we propose a Generative Adversarial Network (GAN) enhanced frame prediction system for single and multi-time ahead image forecasting to aid in environment prediction and early decision. We demonstrate the relative motion of entities within the frames and evaluate our system's efficacy through qualitative and quantitative analyses. As This GAN-enhanced system-inside a DT-can predict a few frames into the future and check for anomalies, this can help the AVs make swift decisions. Moreover, by harnessing this capability to create diverse synthetic scenarios, we can enhance the development of the DT system, thus unlocking a multitude of opportunities for training various models for autonomous vehicles (AVs).
Closed-Loop Control and Motion Planning for Parallel Structural Chemical Reactions

For nonlinear systems, the behavior of the system may be necessary to be known at any time. With the help of motion planning, we can determine the time function of the state variables from the mathematical model. The present work deals with chemical reactions. It shows a motion planning method for parallel structural reactions and demonstrates the given result with the help of closed-loop control. For motion planning, we use the parametrized function class method. We ensure the isothermal behavior of parallel reactions with motion planning and closed-loop control. In a control theoretic point of view, this technique simplifies the model, eliminating the problems caused by nonlinearity. As an example, we design closed-loop control for a two-step parallel reaction.
Improved Opening Window Trajectory Simplification Algorithm

With the popularity of GPS embedded devices, moving objects generate large scale raw trajectory data, which brings challenges to storage, transmission and processing. Aiming at these challenges, an improved opening window trajectory simplification algorithm (OWTR-V) is proposed. In order to better simplify the original trajectory, the OWTR-V algorithm analyzes the influence of distance threshold and velocity threshold on the algorithm performance, evaluates the following performance characteristics: PED error, TRD error and EA error as well as the running time, and determines the appropriate simplification threshold. The experimental results show that compared with OW algorithm and OWTR algorithm, the improved OWTR-V algorithm can achieve lower error and better retain the position information and spatial features of the original trajectory. The improvement of simplification efficiency can save the storage cost and transmission cost of trajectory data, and reduce the pressure of processing trajectory data in the future life.
Analysis Of Radiation Pattern and Gain Offset VSAT Antenna Ku-Band Frequency

Very Small Aperture Terminal (VSAT) technology is a ground terminal technology used in satellite communication devices. Emits focused beams through a directional Radiation pattern in VSAT, which enhances antenna performance by minimizing observed interference through radiation pattern, gain, and also side lobes. This research uses the Ku-Band frequency range, which spans from 12.5 to $\mathbf{1 2. 7}$ GHz. It has a relatively small antenna, measuring between 0.8 and 1.4 meters in diameter. Offset VSAT antennas are created by using different feed types, such as circular feed horns and pyramidal feed horns, and by increasing the antenna diameter. The application of circular feed significantly improves the radiation pattern and gain in the offset antenna using pyramidal feed in $1.4 \mathrm{~m} 27.9 \mathrm{~dB}$ optimization using circular feed horn, the gains become $31.2 \mathrm{~dB}$. The side lobe levels, initially measured $31.60 \mathrm{~dB}$, are reduced through the use of circular feed −41 dB. So, an offset antenna using a circular feed has the most optimal radiation pattern because it produces high gain with low side lobes and a narrower beamwidth so that interference can be suppressed optimally.
Diabetic Retinopathy Diagnosis System Based on Retinal Biomarkers Using EfficientNet-B0 for Android Devices

Currently, Diabetic Retinopathy has become one of the main causes of vision loss for the working-class population (20-65 years) and the disease is an asymptomatic disease which makes detection of the disease without medical assistance more difficult. Along with the development of the era, many new methods have been found in diagnosing diseases, one of which is Computer Aided Diagnosis (CAD) which relies on computer technology to assist medical personnel in diagnosing diseases. This research uses CAD with machine learning to classify Diabetic Retinopathy using transfer learning on the EfficientNet-B0 model which is implemented in a program for mobile devices for android. The program was designed with the aim of helping medical personnel to be able to diagnose Diabetic Retinopathy in areas that do not have sufficient medical facilities using only a smartphone, 20D lens, and several other drugs supports. The results showed that the EfficientNet-B0 model for classifying Diabetic Retinopathy was able to produce an accuracy of 91.85 percent in classifying three classes namely no DR, Non-proliferative DR, and Proliferative DR. This research was done to contribute in the growth of Artificial intelligence involvement in the medical world and also to help areas that lack the medical equipment to have better treatment.
Evaluation and Replication of Wind Tunnel Data Using Artificial Intelligence Methods

The wind tunnel is a laboratory device used for aerodynamic tests. It models the airflow through probes placed on models or prototypes to evaluate the aerodynamic properties by controlling the airflow in different conditions. This study aims to train and test algorithms with data to reproduce the data obtained through probes by using certain artificial intelligence algorithms and to learn their accuracy. For this purpose, ANFIS (Adaptive Neuro-Fuzzy Inference System), ANN (Artificial Neural Networks), and RBFNN (Radial Basis Function Neural Network) models are used. The results obtained with these models are R2 (R-square), RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) parameters. According to these results, the most successful model in training and test data is ANFIS, and the least successful model is ANN. However, the success statistics of this entire model show that they can be used in the evaluation and replication of wind tunnel data.
The Impact of AIGC on Organizational Knowledge Creation : From the Perspective of Adaptive Structuration Theory

Artificial Intelligence Generated Content (AIGC), as an emerging content generation method, possesses creative generation characteristics that inherently facilitate the knowledge creation process. However, the specific impact of AIGC technology on the knowledge creation process remains unclear. Based on the Adaptive Structuration Theory, this paper delves into how AIGC influences the process of knowledge creation within organizations, while also shedding light on the far-reaching implications of AIGC technology at both the organizational and individual levels. This study offers a fresh perspective for understanding the potential and challenges posed by AIGC technology, providing valuable insights for organizations seeking to optimize the knowledge creation processes and advance the field of career science in response to the new challenges presented by the era of generative artificial intelligence.
SeqAdver: Automatic Payload Construction and Injection in Sequence-based Android Adversarial Attack

Machine learning has achieved a great success in the field of Android malware detection. In order to avoid being caught by these ML-based Android malware detection, malware authors are inclined to initiate adversarial sample attacks by tampering with mobile applications. Although machine learning has high capability, it lacks robustness against adversarial attacks. Currently, many of the adversarial attacking tools not only inject dead code into target applications, which can never be executed, but also require the injection of many benign features into a malicious APK. This can be easily noticeable by program analysis techniques. In this paper, we propose SeqAdver, an automatic payload construction and injection tool, which aims to bring the adversarial attack to the next level by injecting a payload that allows execution without breaking the app’s original functionalities. These payloads are obtained from benign APKs at the Smali level and normalized into usable code snippets. The extracted Smali codes are carefully selected by filtering out ‘user-visible’ APIs and Intents. Therefore, payloads are able to be executed without any visible change noticed by the user. Besides, extracted payloads can be injected into different locations of the file based on sequence position or on the launcher class. Experiments were conducted to prove that randomly extracted payloads from benign apps are able to execute without causing any ‘user-visible’ behaviors or crashing the app when running the app in Android emulators.
Maximum Double Coverage by Wireless Sensor Network with Fixed Number of Sensors

The paper is devoted to the issues of increasing the reliability of sensor networks by increasing the fold coverage. A bounded continuous sensing region and a fixed number of sensors with unequal circular sensing ranges are considered. The problem is to find such location of sensors at which the maximum double coverage of the region is achieved. The results obtained can be generalized to the case of an arbitrary multiple coverage. Since the number of sensors is assumed to be limited, and the sensing ranges are bounded, there is no guarantee of double coverage of the region. Therefore, we pose the maximum double coverage problem. We study some cases depending on whether complete single coverage of the region is required. The formulations and mathematical models of the corresponding classes of double coverage problems are considered. Original optimization approach for solving problems are proposed. The implementation of the approach has been tested for the polygonal shape of the region and the binary disk sensing model of sensors.
Prediction Of User Ratings For Drug Side Effects Using Deep Neural Network With Contextual Co-occurrence Based Word-Embedding Vector

In today’s medical landscape, the significance of prescription for drugs in addressing various medical conditions cannot be overstated. However, due consideration must be given to potential side effects and their impact on consumer health. With the advent of Web 4.0, online platforms offer valuable resources, such as drug reviews and information on medication side effects, to aid users in making informed decisions. The accurate prediction of user ratings for drug side effects plays a crucial role in promoting patient safety and enhancing healthcare decision-making. Leveraging the capabilities of generative language models, this research endeavours to automate user ratings concerning drug side effects. In this study, we propose a novel approach based on Deep Neural Networks (DNNs) with contextual co-occurrence based word-embedding vectors to address this important challenge. This research contributes significantly to the growing field of artificial intelligence in healthcare and is poised to make a significant contribution to the field of drug research and user-focused healthcare decision-making.
Integer Point Enumeration in Multi-Dimensional Geometric Objects with FPGA Acceleration

This paper presents a hardware based approach to accelerate the process of integer point enumeration within multidimensional geometric objects using FPGA technology. We introduce a customized FPGA design based on the established integer point enumeration in fundamental parallelepiped algorithm. Through experimentation, we evaluate the performance of this FPGA-accelerated design in comparison to C and traditional Python implementations across a set of 12 test instances. Our experiments reveal a significant computational speedup for the C implementation, ranging from 83% to 100%, compared to the Python implementation. FPGA implementation can speed up the C version about 10x up to dimension 9. But beyond that, the I/O operations due to the lack of memory, make FPGA slower than C although the computation times are still better.
Energy Efficient Wake-up Receiver MAC Protocol in OMNeT++ for Indoor Wireless Sensor Network Communication

A wireless sensor network is composed of a large number of spatially distributed wireless sensor nodes that communicate with each other over a long duration to collect the maximum data accumulated from the physical environment. To accomplish such a mission wireless sensor nodes are usually equipped with a radio transceiver, micro-controller, a set of sensors, and a battery, Power efficiency is required in power-limited wireless sensor networks. Numerous studies have proven that the receiving mode in the radio transceiver is the most consuming process when collecting data, as a result, the wake-up receiver(WuRx) gains much interest due to the low power consumption. The contribution of this paper is the implementation of a wake-up radio mac-based protocol(WUR-MAC) in OMNeT++ to investigate the energy efficiency of WuRx that include a low-frequency pattern matcher(LFPMs) such as the AS3933. Performance comparison of the implemented mac-based protocol(WUR-MAC) with duty-cycled MAC protocol is explored.