Miracle-dz/UniFAR-FTUs huggingface dataset:
Educational Simulator for Analysing Pipelined LEGv8 (subset of ARMv8)Architecture

This paper presents the design and implementation of a pipelined Lessen Extrinsic Garrulity (LEGv8) architecture simulator, subset of ARMv8 architecture. The simulator is developed as a web application that can simulate the assembly of instructions in the LEGv8 assembly language, the execution of the instructions, and the visualisation of data path during execution. The simulator supports both single cycle and pipelined execution, with the option to select the control and data hazard handling methods to use. Users will be able to analyse the changes in the registers and memory, branching behaviour, hazard detection and elimination, as well as visualise data flow when stepping through instructions. This gives users the freedom to comprehend computer architecture more easily at their own pace by making use of the user-friendly and interactive educational simulator to enhance their understanding beyond what can be taught in the classroom.
Approach For Probabilistic Semantic Big Data Indexing under an Uncertain Environment

Indexing techniques are essential for efficiently storing and retrieving big data. Traditional indexing methods may not be suitable for uncertain environments due to their reliance on complete and accurate data. In such cases, probabilistic indexing techniques can be employed. These techniques assign probabilities to different values or attributes of the data, allowing for efficient retrieval even in the presence of uncertainty. Examples of probabilistic indexing techniques include inverted indexes with probabilistic weights and Bayesian networks. Handling uncertainty is a critical aspect of indexing big data in an uncertain environment. Our main objective in this paper is to propose a new method of semantic indexing of data in an uncertain environment. Basically, our proposed method is composed of two main parts: (i) a first part consists in processing the uncertain data in a textual document and, (ii) the second part consists in determining a new model of uncertain semantic indexing.We developed a series of experiments to evaluate our proposed solution by comparing it to the approaches studied in the literature. The results found showed that our solution is more efficient.
Enhancement of Colour Reproduction for Capsule Endoscopy Images

During a clinical examination, a capsule endoscope, like the PillCam, allows the entire digestive system of the patient to be photographed. The images can be analysed to detect abnormalities. However, the rendering is of low quality compared to colonoscopy. Distorted colour reproduction of tissues and blood vessels can be one reason for overseeing abnormalities, and improvement of colour reproduction could improve the examination. For this purpose, a colourimetric calibration must be performed in order to deduce the correction to be applied. The ColorChecker is a chart containing a number of standard colour patches sampled over the gamut of perceivable colours, providing ground truth reference. Thus, it is possible to measure the rendering of these colours and then compare and adjust its values. However, the colours that appear inside the gastrointestinal system are a subset of perceivable colours. It also appears like typical PillCam’s are constructed accordingly. As shown in this paper, colour correction based on the ColorChecker becomes saturated. Therefore, we develop a novel colour checker based on colours sampled from colonoscopy images and perform corrections based on that. An evaluation of both inter- and intra-camera variation indicates that the same calibration method should perform quite well for all PillCam Colon2 capsules. A subjective evaluation of colour corrected videos was performed by two experienced gastroenterologist, indicating that the proposed method obtains a more faithful colour reconstruction.
Retinal pathologies detection in OCT images based on Bilinear convolutional neural network

Retinal pathologies like choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME) can give rise to microvascular alterations in the retina, ultimately resulting in vision impairment. The manual detection of these diseases poses a significant challenge and necessitates specialized medical expertise. To address this challenge, our study introduces novel deep learning methods for the detection of these ocular pathologies automatically and based on optical coherence tomography (OCT) scans. In our experimental setup, we utilized a dataset comprising 6000 OCT images sourced from the publicly available Kaggle dataset. Through comprehensive evaluations, our study revealed that the implementation of a bilinear convolutional neural network (B-CNN) yielded the highest classification score, surpassing the accuracy achieved by alternative models. Furthermore, when compared to other deep learning networks, our proposed approach showcased superior performance in the early diagnosis of these three ocular diseases.
Road Target Detection Algorithm Based on Improved YOLOv5

Aiming at the problem that the existing road target detection algorithm is difficult to deploy lightweight. , this paper proposes an improved YOLOv5 algorithm for road target detection based on the YOLOv5 network framework. By reconstructing the network structure of the Backbone and Neck parts, the number of model parameters and calculations are reduced to make the model lightweight, by introducing Stem Block and CBAM, the model structure is optimized to improve the model detection accuracy, re-clustering Anchors to improve the target positioning ability, The comparison and ablation experiments were carried out using the parameter amount, calculation amount, AP, mAP, and FPS as evaluation indicators. While meeting the requirements of real-time detection, the algorithm in this paper has good target detection accuracy, and the parameter amount and calculation amount are reduced by 29.0% and 36.2% respectively compared with the original model, which is suitable for road target detection tasks.
Automated Compositional Verification for Robotic State Machines using Isabelle/HOL

RoboChart is a graphical language for model-based engineering of robotic systems, in the style of UML and SysML. It contains notations for data structures, system architecture, and the behaviour of individual robotic controllers using state machines. Crucially, RoboChart has a formal semantics in the CSP process algebra, which provides a precise foundation for software engineering and formal verification using model checking. However, due to state explosion, the application of model checking does not scale. In this paper, we contribute a compositional verification technique that uses Isabelle/HOL RoboChart state machines symbolically. Our technique uses state invariants to capture safety requirements over a very large or infinite state, similar to the B method, and is highly automated using Isabelle’s sledgehammer tool. We give a model transformation from the RoboTool development environment to Isabelle/HOL and apply this to several verification case studies.
A novel multi stage optimization algorithm for a 3D-BPP resolution

Several processes constituting the supply chain contain a common step participating in their improvements. It‘s the step of filling the products in their containers. We are talking about the bin-packing problem, which consists of finding the most economical storage of a set of objects in a finite number of bins that minimize the total of bins used.In our paper, we are interested to a three-dimension bin-packing problem, which is a combinatorial optimization problem and is known to be NP-Hard problem. We refer to two recent research works to create our multi-stage optimization named FillAbin to solve the three-Dimension bin-packing problem. The empirical study is based on two main plans. The first one is inspired from a real case in the PROMENS Company. The second plan is based on a benchmark used by two recent works. The obtained results showed the improvement realized by the proposed algorithm.
PcGC: A Parity-Check Garbage Collection for Boosting 3-D NAND Flash Performance

Garbage collection or GC running in the controller of 3-D NAND flash-based solid-state disks—SSDs—plays a critical role in the performance of storage systems. SSD manufacturers have developed various GC solutions based on internal data movement or IDM to mitigate the impacts of GC on request latency. Due to the circuit characteristics of flash memory, the existing IDM-based GC strategies are restricted by page parity during data movement: odd pages must be migrated to odd pages, and even pages to even pages. When migrating two consecutive pages with the same parity, the free page between the two migrated pages will be wasted after the migration is complete. This ever-increasing page waste problem inevitably deteriorates the storage space utilization of flash memory, thereby degrading the overall performance of 3-D NAND flash-based SSDs. To address this issue, we propose a parity-check GC scheme called PcGC to revamp SSD performance by alleviating page waste during GC. We build a parity-check unit in PcGC to facilitate checking the parity of migrated valid pages and destination pages. According to the parity results offered by the parity-check unit, PcGC dynamically adjusts the migration order of valid pages during the course of GC. In doing so, PcGC fundamentally averts page waste caused by the page parity restriction, thereby enhancing 3-D NAND flash performance. We quantitatively evaluate the performance of PcGC in terms of wasted pages, storage utilization, GC counts, write amplification, and average response time. We compare PcGC against the two state-of-the-art schemes—Amphibian and Tiny-tail flash (TTflash). The experimental results derived from the nine real-world workload traces unfold that compared with Amphibian and TTflash: 1) PcGC curtails the number of wasted pages by up to 91.4% with an average of 53.75%; 2) cuts back the number of GC counts by up to 52.2% with an average of 11.9%; and 3) slashes average write response time by up to 77.8% with an average of 13.0%.
Optimized Quantum Circuit Implementation of Payoff Function

Large-scale quantum computers that can execute practical quantum algorithms have the potential to solve complex problems that are currently challenging for classical computers. This involves converting these problems into a form that can be processed by quantum circuits, a crucial process that requires minimizing quantum resources like qubit count, gate count, and circuit depth. Our work focuses on implementing and optimizing the foundational task of quantum finance, known as option pricing, as a quantum circuit. This enables the utilization of quantum computing benefits, within the financial domain. Specifically, we implement and optimize the function fK(S) = max(S−K, 0). Taking into consideration the significant trade-offs between qubit count and circuit depth, we have developed quantum circuits for the optimized implementation of the fK(S). Our work incorporates various optimization techniques for the circuit, such as selecting the optimal adder, optimizing the S−K operation, parallelization, and qubit reuse. Furthermore, we offer various versions of our quantum circuits for the fK(S), each featuring different adders and Toffoli decompositions, thereby providing flexibility for a wide range of use cases.
Defense Against On-Chip Trojans Enabling Traffic Analysis Attacks Based on Machine Learning and Data Augmentation

Modern computing systems involve huge data exchange across various sections of the processing system. To facilitate this, network-on-chip (NoC) serves as a crucial infrastructure that connects the processing cores to memory, peripherals, etc. The system could be put at great risk should the NoC system become compromised. The NoCs are used in multi/many-core processors; this domain is experiencing increased threats because of hardware Trojan (HT) embedded in the multicore processing systems due to the presence of third-party entities in the system-on-chip (SoC) design pipeline. Protecting user and system level privacy becomes important in such multi core systems to enable trust. By embedding an HT in an NoC, the adversary can snoop on important insights regarding the applications executing on the system or the user profile information. An attack of such calibre can compromise privacy, thereby enabling more advanced attack on the entire system. This work demonstrates the capability of a traffic analysis attack when a few HTs are embedded in the NoC switches of a multi/many-core processor. The attack is capable of exposing sensitive information to an external malicious attacker who can then analyze the payload data with sophisticated machine learning (ML) techniques to infer the applications executing on the system. We also evaluate the performance of a generative adversarial network (GAN) strengthened attacker model that offers more robustness for data paucity scenarios. We propose a simulated annealing-based randomized routing algorithm based defense for NoCs, thus thwarting the attack. The results demonstrate that the proposed randomized routing algorithm could reduce the accuracy of identifying user profiles by the attacker from $>$ 98% to < 15% in multi/many-core systems.
Deep Learning Based Traffic Prediction for Resource Allocation in Multi-Tenant Virtualized 5G Networks

Network traffic has changed significantly since the introduction of 5G technology. The massive flow of connected devices as well as new applications are causing a variety of traffic patterns, quality of service (QoS) requirements and scalability challenges. The solution to these problems is network slicing which enables operators to distribute resources and set network characteristics per slice by constructing multiple virtual network slices within the shared 5G infrastructure. Resources are reserved for each slice for some time period. Traffic prediction with high accuracy is of great importance in the dynamic environment of 5G networks for resource planning and scheduling as well as for reliable and effective transmission of network data. In this paper, We generate user requests in a slice having arrival and departure rates using Poisson distribution. We did a comparative analysis of traffic prediction and resource allocation using deep learning models such as Long Short Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Stacked LSTM and Gated Recurrent Unit(GRU).
An Efficient Approach of Drowsiness Detection Based on ResNet50 and Xception Architectures

This paper delves into a critical aspect of road safety by investigating a method to detect drowsiness in images. The accurate utilization of this technology by fatigued drivers holds the potential to prevent numerous fatal accidents, as the proposed models exhibit swift responsiveness in identifying instances of driver drowsiness. Within the realm of eye closure detection, two drowsiness models, namely ResNet50 and Xception, are employed to portray various eye state classifications. Nonetheless, the scarcity of readily available and reliable eye datasets poses a formidable challenge. Leveraging the power of ResNet50, deep facial features are extracted, resulting in an impressive accuracy of 98.06%. Similarly, the utilization of Xception yields a commendable accuracy of 95.69% when evaluated with the MRL dataset. These experimental outcomes firmly establish the higher accuracy and cost-effectiveness of eye closure estimation, thereby underscoring the efficacy of the proposed framework for drowsiness recognition.
Novel Soil Impact Prediction Model on Bandwidth

The antenna’s bandwidth strongly depends on its physical characteristics and the surrounding environment. However, permanently embedding devices in the soil causes inherent instability in the propagation medium due to spatial and temporal variations in dielectric permittivity. This study introduces an innovative multivariable approach that effectively captures dataset patterns, enabling accurate prediction of frequency range variations, at around 70%. This technique relies on the multiple linear analysis method and considers soil parameter values. Furthermore, our research findings demonstrate that among the four key variables, volumetric moisture exhibits a pronounced influence on the operating band, with a notable correlation coefficient of 0.72, whereas temperature and bulk density show scores of -0.381 and 0.08, respectively. On the other hand, the effect of specific density appears to have minimal significance. The obtained results successfully achieve the set objectives of this study by providing critical information for designers to address the challenges that transmitters may encounter in such scenarios.
Deep Learning-Based Object Detection And Bacteria Morphological Feature Extraction For Antibiotic Mode Of Action Study

The increasing prevalence of antimicrobial resistance (AMR), as microorganisms develop resistance to antimicrobial drugs, has emerged as a critical concern in infection treatment, resulting in a rising death toll. Assessing the effect of drugs can provide insights by studying the morphological change of bacteria after drug treatment. However, utilizing conventional techniques such as CellProfiler for long-term and large-scale sample experiments is impractical due to the manual processes involved. To address this challenge, we proposed a deep learning-based object detection model for predicting the type of antibiotic treatment and automatically extracting bacteria morphology. Our model combines YOLOX and two Cascade R-CNNs using weight box fusion to enhance performance. It achieves an mIOU of 0.753 and mAP of 0.699 higher mAP compared to CellProfiler (mAP = 0.218). In addition, we use a computer vision approach to extract bacteria morphological features including cell membrane, DNA, and color intensity to classify the treated antibiotic which achieves comparable performance to CellProfiler (F1-Score = 0.75, 0.79 respectively). We believe our work can be used as an automatic tool to enhance the efficiency of antibiotic prediction and extracting cell profiles for AMR applications. Our code and web application are available at https://github.com/biodatlab/bacteria-detection.
A Hydropedological Approach to Describing Runoff Generation, Lateral Podzolization, and Spatial and Temporal Patterns of DOC in a Headwater Catchment.

The variations in discharge and water chemistry among and within headwater catchments are not well understood. Developing a better understanding of the processes that control these variations is crucial to determining how headwater catchments will respond to changes in climate and land use. This dissertation explores how hydrologic processes in headwater catchments may be better understood by utilizing a hydropedological framework, where similar soils are grouped together and considered to be representative of and developed by similar hydrologic and biogeochemical processes. In the first chapter, soil groups, called hydropedological units (HPUs) are found to be indicative of distinct water table regimes characterized by the interquartile range and median of shallow groundwater levels, the percent time water table exists in the soil, and the level of catchment storage at which groundwater responds. The second chapter explores the hydrological processes that may lead to the formation of HPUs in the catchment. By examining water table records and unsaturated water potential from tensiometers we found that lateral unsaturated flow regimes may be partially responsible for the patterns of lateral translocation observed in HPUs. Finally, the third chapter identifies two HPUs in the catchment as sources of streamwater dissolved organic carbon (DOC). While nearstream areas have typically been found to be DOC sources in headwater catchments, the HPUs identified as sources occur at high elevations in the catchment, near channel heads. Overall, these findings will be useful to better explain runoff generation, soil formation, and DOC export from headwater catchments. Headwater streams source water to larger bodies of water that are valuable natural resources. Therefore, explaining these processes is critical to predicting and responding to changes in climate and land use that may affect important water supplies.
A 2-step deep learning approach to splenic injury detection

The spleen is the most commonly injured solid organ in blunt abdominal trauma. Physicians can use abdominal CT to understand the location of the injury and determine its severity grade. However, these injuries may go undetected because of the inexperience of junior physicians. In our previous study, we had performed a 2-step object detection based analysis pipeline on abdominal CT images to identify whether the spleens are injured or not, and also reported that localizing the spleen first can enhance the prediction performance. In order to delineate the region of the spleen more precisely, in this study we adopted TotalSegmentator without the need of the training process to segment the contour of the spleen. In addition, we further utilized morphology operations to expand the segmented region, especially useful for the spleen injury cases. Our proposed method improves the baseline which only using TotalSegmentator to get the segmentation of spleen by 6% in terms of accuracy, which confirms its effectiveness.
A Real-Time and Robust LiDAR SLAM System Based on IESKF for UGVs

Positioning system is crucial for autonomous navigation of unmanned ground vehicles (UGVs), which provides the accurate position and orientation to perception and planning-control modules. However, the indispensable drifts will significantly affect the accuracy of the positioning system when lacking the global navigation satellite system (GNSS) signal. To address the common issues, this paper proposes a real-time positioning framework without using GNSS for robust positioning performance. Firstly, with the prior map, we adopt iterated error state Kalman filter (IESKF) to predict and optimize vehicles pose. In addition, we utilize the measurement input of Inertial Measurement Unit (IMU) to estimate the current state and covariance, and take it as the odometry output to ensure the real-time performance. Secondly, a novel map construction strategy is developed to boost mapping efficiency. And the corresponding comparisons are implemented to indicate the effectiveness. Finally, we use an UGV platform for data acquisition and generating dataset, and the proposed algorithm is evaluated on our own dataset. The results of the comparative experiments demonstrate the effective of the method in real-world applications.
Suicidal Text Detection on Social Media for Suicide Prevention Using Deep Learning Models

The advent of social media has transformed the way we communicate and connect, enabling individuals worldwide to instantly and openly interact with friends, family, and colleagues on a frequent basis. People utilize social media platforms as a means to express their opinions, share personal experiences, narratives, and challenges. Nevertheless, concerns have arisen due to the growing prevalence of suicidal content on social media platforms, where discussions of hardship, thoughts of death, and self-harm are widespread, particularly among younger generations. Consequently, harnessing the power of social media to detect and identify suicidal behavior, including the presence of suicidal thoughts, becomes essential in offering appropriate interventions that discourage self-harm and suicide, as well as in preventing the spread of suicidal ideations throughout these platforms. This paper presents suicidal content detection using two deep learning architectures, LSTM, and DistilBERT with the latter showing better performance in respectively. We conclude by drawing implications for deep learning architectures in detecting suicidal content on social media and an initial deployment of the models using Telegram bot which detects the message containing suicidal content and sends a motivational message in response and also informs their friends and relative through alerts.