Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Security-Mitigation of Custom and Configurable Hardware Cryptosystems

Cryptographic algorithms and associated hardware accelerators are ubiquitously used to preserve the confidentiality, integrity, and privacy of sensitive data on electronic devices used by consumers, governments, military forces, and spacecraft. The proliferation of cryptographic algorithms in modern devices has made it more difficult to orchestrate exhaustive attacks on sensitive data using classical cryptoanalysis. However, hardware implementations of such cryptosystems are often vulnerable to attacks such as timing and power side channels, fault injection, and reverse engineering. Furthermore, protecting assets and maintaining security requires different configurations and capabilities for different applications. In this paper, we outline conventional symmetric and asymmetric cryptocores along with cryptographic hash function. We also include existing security primitives to aid and ensure secure system operation. We discuss the mitigation of existing threats and vulnerabilities on each of these cryptographic and security primitive modules and provide design overheads and security assessments for each configuration to aid design space exploration.
RIDA: Cross-App Record and Replay for Android

The number of Android apps keeps increasing in recent years. Despite the fact that there exist apps for various kinds of purposes, apps that share similar functionalities with existing ones are still emerging on the market. To reduce the effort in testing such apps, previous research has proposed approaches for migrating test scripts across similar apps. However, such test reuse techniques require existing test suites for migrating, which hinders their practical use in commercial app development. Unlike script-based GUI testing, record-and-replay techniques are more convenient for human testers who are unfamiliar with programming. In this paper, we propose a new testing technique, RIDA, that records interaction sequences on one app (source app) and replays them on another app (target app) with similar functionalities. Such cross-app record-and-replay is challenging. First, there are no clear mappings between the recorded sequences on source apps and the sequences required to be performed on the target apps. Second, reliable indicators of widgets’ functionalities are not always available from the recorded sequences, which limits the effectiveness of event matching between source and target apps. To address the challenges, we design an on-the-fly searching algorithm for finding target widgets during cross-app replay and leverage multiple semantic descriptors together with image captioning techniques to infer the functionalities of widgets. We have implemented RIDA and evaluated it using both controlled and in-the-wild experiments. The results show that RIDA can effectively perform cross-app record-and-replay and outperform baseline methods in terms of the number of completely- and partially- replayed events.
Prediction of Road Traffic using an ELM-based Neural Network

This study discusses about traffic prediction, which is possible in intelligent transportation systems. This involves making predictions based on data from the previous year and data from the most recent years, which eventually yields accuracy and mean square error. For those who need to check the current traffic situation, this prediction will be useful. The traffic statistics is based on a 1 hour time gap. From this prediction, live traffic numbers are examined. So, while the user is also driving, this will be simpler to examine. The core objective of this proposed system is to identify the future traffic based on the video analysis. The proposed system uses video analysis and ELM based neural network. The proposed system is also useful for central and state government for maintaining smooth traffic flow.
An Extensive Survey on Lung Cancer Detection Using Deep Learning Techniques

Lung cancer prediction is becoming a prominent research area as there are many positive aspects to automated clinical diagnosis. A key technology during recent time is the automated identification of lung cancer utilizing scan images. It's indeed difficult to establish an effective approach which could automatically identify lung cancer, even though a variety of strategies have been described in the previous studies for this purpose. A patient may have a better hope of recovering and survival with an early diagnosis. Potential cancer detection is significantly aided by innovation. Ergo, the purpose of this research is to establish a deep learning-based automated lung cancer detection system. The focus of this research is to describe, analyze, contrast, and evaluate a number of techniques for classifying and detecting lung cancer in its early stage, as well as numerous methods for image segmentation with feature extraction. This paper provides an ample survey on the current approaches and addresses the problems and thereby proposes a solution based on CNN, experimented with a sample dataset.
E2E-Loader: A Framework to Support Performance Testing of Web Applications

Performance testing is crucial to assess that Web Applications provide a good user experience under different workloads. A workload reproduces the interactions of a number of concurrent users with the system, to observe its actual behavior under stress.Defining meaningful workloads is a key challenge in performance testing, and many solutions have been proposed in the literature to support testers in this task, mostly based on analyzing system logs describing real user behaviors. However, in our industrial and academic experience, we found that these solutions present some limitations, hindering performance testers’ applicability and productivity. In particular, (I) they require the system under test to be actually deployed in order to collect real user behaviors; (II) they offer limited support to automated management of data dependencies; (III) they lack support for emerging protocols, such as WebSocket.In this paper, we present E2E-Loader, a novel approach to automate the design of performance testing workloads for web applications. E2E-Loader generates workloads by exploiting existing End-to-End functional test cases and can be used at an early stage, before the system is deployed and actual user behaviors have been collected. Our solution features full WebSocket support and includes a customizable heuristic to automatically detect data dependencies.We empirically evaluate the proposed approach in an industrial case study. Results are promising and show that the workloads generated with E2E-Loader are generally comparable to those that were manually created by practitioners working with our industrial partner while requiring a fraction of the time to be obtained. Finally, we make E2E-Loader and its source code publicly available for interested practitioners and researchers.
Intelligent Agent for Food Recognition in a Smart Fridge

Respecting an adequate diet is essential for a healthy lifestyle. However, keeping track of the daily consumed food and of what one has in the home fridge may be tedious and time consuming if not backed up by user-friendly applications. In this context, the paper presents some of the main components of an intelligent agent and system aimed to create a user-oriented application for recommending personalized food recipes based on the available ingredients and dietary restrictions in a domestic environment. The developed application includes a component to accurately detect the ingredients from an image taken from inside a fridge equipped with mounted cameras, and allows the user to get information about each ingredient, such as the ingredient type, name and associated calories. To facilitate the development of this application, our own data set of food ingredients was collected and annotated, and an ontology of recipes was developed.
Koopman Operator-based Extended Kalman Filter for Cosserat Rod Wrench Estimation

This paper proposes an observer-based approach for estimating the wrench (force and moment) acting on a 3D elastic rod, using pose states (i.e. robot shape) along the rod as input/feedback. First, the static rod is considered as a dynamical system evolving its states in spatial dimension, Koopman operator theory is adopted to derive an explicit discrete-arclength model for the rod. Then, Extended Kalman Filter is applied to the derived model to estimate wrench states along the rod. Static balance constraints between pose and wrench are enforced to improve force estimation performance. The developed model and wrench estimation approach are evaluated through representative numerical simulations using a single rod. In representative examples, results show average tip force and moment estimation errors of 0.19 N (10.47%), with maximum 0.73 N (31.90%), and 4.52 mNm (2.25%), with maximum 8.28 mNm (7.36%), respectively. Compared to the state-of-the-art, in close test cases, proposed algorithm obtains slightly lower average tip moment and higher force estimation errors of 2.6% and 4.2%, than 2.7% and 2.2%, respectively.
Assessment of Distributed Flocking Algorithms for Multi-Fixed-Wings UAVs using SIL Simulation

This paper aims to implement and validate the flocking approaches known as Olfati-Saber and Vásárhelyi algorithms that are originally developed for fully-actuated systems for coordinating underactuated Unmanned Aerial Vehicles (UAVs) teams in a stochastic environment using a Software-in-the-Loop (SIL) Simulation Platform based on realistic aircrafts with nonlinear dynamic models. These algorithms allow the UAVs to maintain the cohesion, alignment and separation between the agents of the team during flight by attending to the distance criteria during the path-following and avoiding collision among them. For validating the investigated approaches, the proposed SIL platform uses a team of virtual UAVs based on the non-linear aerodynamic model of the Cessna 172 Skyhawk implemented in a ROS/Gazebo architecture, while the Matlab/Simulink executes of the flocking control algorithms. Simulation results demonstrated that the flocking algorithms can efficiently coordinate the underactuated UAVs teams considering their nonlinear dynamics and also the communication among all of them during the execution of different flight maneuvers.
Multimodal Approach for Classifying Stress using Facial Emotion and Physiological Sensors

The focus is on understanding emotional stress by itself may enhance artificial intelligence agents involved in emotion detection and human computer interaction. These emotional responses are reflected into emotions and facial expressions. This research work proposes a study of Stress Classification using both facial expression and Physiological Sensors. For getting facial data, transfer learning is used with fine-tuning to extract features from facial images. In transfer learning different Deep Learning architectures like VGG-19[13], ResNet are used. From the sensors data on four features are collected that is age, gender, body temperature and heartbeat and accordingly choose the architecture for doing stress classification. Finally, both these models are integrated for getting better results.
Pedestrian Trajectory Prediction Based on Tree Method using Graph Neural Networks

Pedestrian trajectory prediction in real-world scenarios is a challenging task for several computer vision applications, such as autonomous driving, video surveillance, and robotic systems. This is not a trivial task due to the numerous potential trajectories. In this article, it provides a tree-based approach to handle this multimodal prediction challenge. The tree is designed based on the observed data and is also used to predict future trajectories. In particular, an individual's potential future trajectory is represented by the tree's root-to-leaf route. Compared to previous approaches that use implicit latent variables to describe possible future paths, the movement behaviors may be directly represented by the path in the tree (e.g., go straight and then turn left), and thus offer more socially suitable trajectories. The experimental results on the ETH, UCY, and Stanford Drone datasets show that this approach can exceed the performance of the state-of-the-art approaches. The solution is more efficient and compact, with a smaller model size and a higher accuracy, and delivers better results with reference to average displacement error (ADE) and final displacement error (FDE) metrics.
Inspecting Binder Transactions to Detect Anomalies in Android

With the growing number and complexity of threats to mobile devices in the latest years, new security strategies are constantly developed to protect the users. The wide variety of Android malware families makes it challenging to keep up with malware evolution and build detection systems that are generic enough to deal with them. This work explores inter-process communication (IPC) between Android processes for anomaly detection. All IPC messages in Android go through the Binder driver, making it a good vantage point to observe all kinds of malicious actions. We observed how malicious and benign applications interact with Binder and built a dataset representing their behavior. We enriched the raw dataset by classifying Binder calls into five groups according to their functionality and by identifying high- and low-risk groups. These new features were used in a machine learning-based method to detect malware on Android and validate it using these datasets, achieving accuracy and F1Score close to 0.90.
Microstrip-line fed Dual-band L-shaped Antenna with Monopolar Characteristics for Application in WLAN

In this paper a microstrip line fed dual-band monopole antenna with L-shaped radiating element for operation in WLAN is proposed. Two paths of resonance are created in the L-shaped radiating element for dual-band operation of the proposed antenna at the center frequency of 2.56 GHz and 5.49 GHz for WLAN application. Two resonating paths are approximately equal to quarter wavelength length at 2.56 GHz (0.234λg) and 5.49 GHz (0.192λg). The proposed antenna exhibits appropriate gain and pattern of radiation characteristics which enables the proposed antenna suitable for application in WLAN band of operation
Deep Learning-based Image Text Processing Research*

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.
An Investigation of the Effects of the Numerical Missing Value Imputation Methods for Click-through Rate Estimation Performances

Missing data is a common issue in datasets and can have a significant impact on the performance of machine learning models. Imputation is a popular technique for handling missing data, where the missing values are estimated based on the observed values in the dataset. In this paper, we shallowly investigate the effects of missing data imputation on click-through rate (CTR) estimation and classification for tabular data. We evaluate the performance of deep CTR estimation methods, such as DeepFM, FiBiNET, ONN, and DCN on a short Criteo dataset with missing data. Here, we compare the performance of different imputation methods, such as mean, median, iterative, and kNN based imputation approaches to fill in the missing value. Our results show that missing data imputation can improve the performance of deep CTR estimation methods in terms of AUC, loss, and f1-scores. Our findings may provide insights into the effects of missing data imputation on the accuracy and reliability of machine learning models for tabular data classification and CTR estimation.
Rice Leaf Diseases Classification Using Deep Learning Techniques

Rice is the primary food source for a significant portion of the global population and the productivity of rice crops can be severely impacted by diseases. These diseases can cause significant yield loss, which can have a major impact on food security. Accurate and timely detection of rice leaf diseases is therefore crucial for implementing effective control measures to minimize yield loss. This study aims to work towards the detection of rice leaf diseases, specifically leaf smut, brown spot, and bacterial leaf blight, using a deep learning approach. ResNet50 with an added NN architecture was trained on a dataset consisting of images of rice leaves collected from the Bahribahri rice farm in Indonesia. The dataset includes 4000 photos of each of the three diseases listed above in addition to an equal number of photographs of rice crops in good health. The dataset is used to train the model so that it can identify the presence of the diseases in new images. The results show that the use of ResNet50+NN achieved an accuracy of 99.5% in detecting the three diseases, making it a promising tool for rice leaf disease detection in a farm setting. In summary, this study provides an efficient and accurate solution for rice leaf disease detection, which is critical for maintaining rice productivity and food security.
An Automated Smart Centralised Vehicle Security System for Controlling the Vehicle Thefts/Hacking Using IOT and Facial Recognition

Centralized Vehicle Security System is a python-based computer application, that uses Machine Learning (ML) techniques to detect and recognize the driver's face. So that only authorized personnel of an organization can drive the vehicles. With the invention of many other features like a key-card entry that will unlock the car and start the ignition of the vehicle, no requirement for keys. The application software will keep track of the vehicle. Also, the authors have developed a drowsiness detection system that has been implemented on the same motherboard using python and OpenCV to prevent major accidents on the road. The obtained results of proposed approach are convincing and promising.
The Effect of Temperature on Photovoltaic Power Generation

As the world increasingly embraces renewable energy, more attention is being given to factors that affect their performance. Solar photovoltaic is a leading source of renewable energy, making it crucial to understand which factors have the greatest impact on its parameters. Temperature is a significant aspect of the study of solar cells. This study conducts a simulation of the performance of a solar cell on PC1D software at three different temperatures within a controlled environment. The parameters were modeled on a 200 cm2 silicon solar cell. The rise of 5 °C decreases the power output by 2% while the increase of 20°C decreased the power output by 10.4%.
State Feedback Controller Stratagem for Permanent Magnet Synchronous Motor Drive

Recently Permanent-Magnet Synchronous Motor (PMSM) drives has be expanding in hybrid electric vehicles for high dynamic applications. This article presents three-level & five-level Diode Clamped (DC) Multilevel dc to ac converter using varieties of Multicarrier PWM strategies. All those various strategies are discussed briefly and also give performance comparisons and THD comparisons of different closed loop control schemes. Mathematical modeling of Multi level dc to ac converter coupled Permanent-Magnet Synchronous Motor was described in this concept. Usually, PMSM drive operates at broad operating regions and rapid transients; linearization techniques and linear control methods are required to investigate non-linear models like a DC/AC converter fed electric drive, so it exhibits some limitations. In this article, Non-linear controller strategy is proposed for cancellation of system non-linearity. Pole placement technique in state feedback controller method is proposed in this concept to achieve superior dynamic performance of PMSM drive fed from Multi Level Multi Carrier Sinusoidal pulse width modulation converter. To obtain much performance of three-level & five-level Diode Clamped (DC) Multilevel dc to ac converter coupled PMSM drive, consequently state feedback controller was designed. By analyzing varieties of Multicarrier PWM strategies, this article proves that Alternate Phase opposition disposition sinusoidal PWM for five-level inverter gives better output voltage waves shapes with Less Total Harmonics and it gives smooth stator and damper winding currents of D-Q axis and settled with finite value in steady state conditions. Closed loop control stratagem for different levels with DC strategy was developed and compared with all those different schemes is tremendously simulated using MATLAB.
Performance Analysis of High-Speed MIMO FSO System In Various Data Formats

Free space optical communication (FSO) is a technology that uses laser light for the propagation of information signals. FSO technology provides many advantages, but climatic variations make long-distance transmission ineffective. In this paper a hybrid combination of multiple input multiple output (MIMO) technique and Wavelength division multiplexing (WDM) technique with FSO system was utilized. Moreover, Gaussian pulse generator (GA), Return to zero (RZ) and Non-Return to zero (NRZ) data formats are used for the performance analysis of the proposed system. The Results show that the NRZ data format is suitable for a higher transmission range and the GA data format is suitable for a lower transmission range. To compile the results, the OptiSystem software is used. In the simulation study, the bit error rate (BER) and quality factor (Q-factor) has been used to evaluate the results.
Genetics and Resistance / Génétique et Résistance Inoculum threshold for stripe rust infection in wheat

Stripe rust (Puccinia striiformis f. sp. tritici (Pst)) and powdery mildew (Blumeria graminis f. sp. tritici (Bgt)) are important diseases of wheat in Canada and worldwide. Molecular detection methods permit spore detection of few spores; therefore, there is a need to determine initial inoculum thresholds for pathogens to cause disease under both controlled environments and in the field. Susceptible wheat cultivars ‘Avocet’ and ‘AC Barrie’ were inoculated with different quantities of spores (0, 10, 10, 10, 10, and 10) of Pst and Bgt. Disease incidence, severity and infection type were evaluated. Results of controlled environment studies showed that the minimum number of spores necessary to cause appreciable incidence and severity for Pst was at higher spore concentrations of 10–10 spores. Conversely, low incidence and severity levels were observed at 10–10 spores for Bgt. Despite occurrence of natural Pst infection, results of field studies in 2016 and 2017 in Southern Alberta demonstrated that significant increases in severity levels were observed following application of 1.2 × 10 spores. Collectively, these results demonstrated that stripe rust severities increased with increasing spore concentration only at high spore levels. In contrast, Bgt severity increased with spore concentration from 10 to 10 spores mL. In vitro and in vivo spore germination tests demonstrated germination rates of Pst spores were reduced at lower spore concentrations compared to germination rates at the higher concentrations. Understanding of minimum spore numbers required for disease development will be a prerequisite for predicting epidemics and devising fungicide control measures for future sustainable agricultural systems.