Reviewerly/exHarmony huggingface dataset:
Accessible Arabic Reading for Students with Disabilities

Readability is an area of research that involves assessing the difficulty level of texts. It involves different aspects, mainly: the reader population, the nature of the texts and the purpose of reading. The objective of adapting a readability measure in the material selection process is to deliver information to a larger population of users in an easily readable and understandable format. For particular populations, such as people suffering from disabilities, it is important to collect concise, up to-date recommendations, and raise awareness of the problems faced by these people. In this work, we suggest an automatic process for measuring Arabic text readability for learners with disabilities. This study is performed based on the Modern Standard Arabic Readability corpus (MoSAR), which is a set of texts designed for learning Arabic as a first language (L1). MoSAR texts are annotated with six difficulty levels according to the grades of the Moroccan elementary school. The proposed approach combines classical, non-classical readability features, and features dedicated to learners with disabilities. The principal objective in this study stands on evaluating the performance of the combining features in predicting readability for learners with disabilities. The obtained results reported a maximum accuracy of 77.47%.
Investigating the Performance of BERT Model for Sentiment Analysis on Moroccan News Comments

Sentiment analysis is the field of determining the attitude or emotions expressed in text, which has become popular due to the rise of social media. AI, ML, and DL have greatly impacted the field of NLP and there have been efforts to improve the accuracy of sentiment analysis using linear models and deep neural networks. The analysis of emotions and feelings in Natural Language Processing is a fascinating area of research that aims to understand people's psychological state through their written opinions. Transformer-based models have been found to be highly effective in sentiment analysis and are currently the best option for many languages. However, sentiment analysis in Arabic still requires improvement, particularly in the tokenization stage. This article conducts a comparative study of various Bidirectional Encoder Representations from Transformers (BERT) models and found that MARBERT is the top performer in terms of classification quality and accuracy compared to other Arabic BERT models.
MDA approach for generating the PSM model for the NoSQL key-value database, application on Redis

Big Data is a phenomenon that has influenced different fields; certainly, data has become an important source in decision-making. On the other hand, this Big Data phenomenon has given rise to the appearance of a new type of database, namely NoSQL databases, such as document-oriented databases (MongoDB, Simple DB, Elastic search, CouchDB, …), column-oriented databases (Apache Cassandra, MonetDB, …), graph-oriented databases (Neo4j, …). Migrating from a classic database (relational database) to a NoSQL database is often a difficult and complicated task. In this context, we present, in this work, an MDA approach (model driven approach) allowing to generate a PSM model (platform specific Model) attached to NoSQL Redis key-value database. In this work, we performed a model-to-model (M2M) transformation based on the QVTo model transformation language. We plan in a future work to generate the source code by a model to Text (M2T) transformation using the Acceleo code generator.
A ListNet-based Combination of Lexical and Semantic Features to Homogenize Folksonomies in Online Social Networks

The popularity of online social networks has grown tremendously in recent years, with over 4.6 billion active users in 2022. Hashtags are commonly used by social media users to categorize their posts and connect with like-minded individuals. However, with the sheer volume of hashtags being used, it can be challenging and time-consuming to select the most appropriate ones. Therefore, there is a need for a system that can help users choose suitable hashtags. In this paper, we propose a learning-to-rank-based approach that considers both lexical and semantic features to recommend hashtags on microblogging platforms. We also investigate the impact of combining different ranking strategies on hashtag suggestions. Our experiments, which used a large dataset from Twitter, demonstrate that our approach outperforms both lexical-based and semantic-based methods.
A Survey on Various Decoding Algorithms for McEliece Cryptosystem Based on QC-MDPC Codes

The McEliece cryptosystem has proven to be a secure and efficient method for protecting sensitive information in communication infrastructure. Among the variants of this cryptosystem, the quasi-cyclic moderate density parity-check (QC-MDPC) McEliece Cryptosystem has received significant attention due to its compact key size. This paper presents a comprehensive survey of the various decoding algorithms used for the QC-MDPC McEliece Cryptosystem. The performance and effectiveness of each algorithm in securing data in the face of quantum computing challenges is thoroughly evaluated. This survey provides valuable insights into the strengths and weaknesses of each decoding algorithm, and offers guidance for future research in the area of code-based cryptography for quantum computing.
Problem-Oriented Medical Records for Describing Care Cases Using Multi-Tenants

There are countless applications in healthcare for digital personal assistants helping clinicians as well as patients and their families. However, there is no conversational application to understand patient encounters and assist clinicians to describe their clinical cases in association with their medical record like the HL7 FHIR. Building such conversational service requires understanding of the charting schema used to record clinical cases in the clinical setting like the SOAP note as well as having the federated APIs to exchange messages not only between tenants (e.g. Patients and Physicians) but also with different servers including the FHIR electronic healthcare record server. In this paper we described the extension that we have added to our existing QL4POMR framework to have extended ability to represent clinical cases via the use of multi-tenants chatbots based on the SOAP note and the connectivity to the FHIR server. The extension to the QL4POMR uses the Google DialogFlow and the GraphQL-Yoga APIs. This research work is a continuation of our MITACS and NSERC funded work that has started in 2020.
GRAMINER: Fuzz Testing Gramine LibOS to Harden the Trusted Computing Base

Intel SGX [1] enables a variety of valuable use cases (e.g., secure data sharing [13]) by protecting an application from all other untrusted parties (e.g., host kernel). However, incorporating Intel SGX in the conventional software development introduces additional requirements. New interface between the application running in an SGX enclave and the host kernel (i.e., ecall and ocall [1]) is one of the requirements.
Towards Modular Trusted Execution Environments

State-of-the-art implementations of Trusted Execution Environments (TEEs) present system designers and users with several problems: First, it is not possible to choose a TEE implementation independently from the instruction set architecture. Second, the security-critical functionality of such TEEs is deeply integrated into the micro-architecture of complex processor cores, making programs running in such TEEs vulnerable to side-channel attacks. And third, the interface and execution model of certain types of TEEs make it hard to integrate these TEEs with the system software. To address these issues, we propose a modular TEE design. We apply this modular design to the M3 hardware/software co-design platform and demonstrate how TEE support can be made a first-class feature at the system-architecture level.
DRL and Main-Side Blockchain Empowered Edge Computing Framework for Assistant Driving

To provide intelligent and accurate assistant driving services in smart city, as well as ensure the security and tamper proof of vehicle data, this paper build a deep reinforcement learning (DRL) and main-side blockchain empowered service framework. By storing driving data and vehicle information on the sidechain, while deploying index information on the mainchain, the main-side blockchain structure can enhance the scalability of blockchain, decrease the communication overhead, improve consensus efficiency, and avoid the leakage of data between different sidechains. However, the resource limited vehicles on sidechain cannot process numerous computation-intensive mining tasks in time, resulting in high service delays. Thus, this paper integrate mobile edge computing with blockchain system to design a double-layer mining service offloading mechanism, allowing the edge nodes and neighboring vehicles to form a cooperative mining network and collaboratively participate in mining process with specific offloading rates. The first layer uses Asynchronous Advantage Actor-critic (A3C) algorithm to efficiently offload partial mining task from the task vehicle to the road side unit (RSU), and the second layer applies double auction to specifically obtain the offloading rates from RSU to multiple service vehicles. Simulation results demonstrate that, our proposed mechanism outperforms other compared algorithms in the average profit and consensus delay.
A Close Look at Shared Resource Consumption in NoSQL Databases for Accurate Accounting

A NoSQL database plays an essential role in all parts of today’s multi-tenant large-scale IT services. Accurate information about per-tenant resource accounting is invaluable for optimal service management. However, there is a lack of study on the characteristics of resource consumption by multi-tenants in NoSQL database services, particularly for those resources consumed as part of activities asynchronously triggered by requests accumulated over time from multiple tenants. According to our investigation, this shared resource usage takes up a significant portion of total resource consumption in NoSQL databases.We assert that an accurate understanding of the shared resource consumption pattern is required to design correct and effective techniques for resource management. To this end, we conduct a detailed investigation of the shared resource consumption in popular NoSQL databases. Our focuses are to find out what portion of the overall resource consumption occurs in a shared manner, what type of operations cause them, and what effect the workload intensity has on the amount of shared resource consumption. We have developed a set of techniques for monitoring, recording, and analyzing the resource consumption data toward answering these questions. Our investigation revealed that the shared resource consumption can be as large as 30% of the total resource consumption, implying that resource accounting based only on directly causal events, may lead to underestimation of the true amount. We believe our study highlights the importance of shared resource accounting and provides crucial insights for building accurate resource accounting techniques.
A RISC-V Extension to Minimize Privileges of Enclave Runtimes

In confidential computing, the view of the system software is Manichean: the host operating system is untrusted and the TEE runtime system is fully trusted. However, the runtime system is often as complex as a full operating system, and thus is not free from bugs and exploitable vulnerabilities. Yet, it executes with complete system-level control over the enclave application, in violation of the least privilege principle. While the confidential computing research community has been striving to secure trusted software from its untrusted counterpart, efforts fall short when it comes to securing the enclave application from potentially bug-prone and vulnerable trusted runtime systems.
Multi-granularity feature utilization network for cross-modality visible-infrared person re-identification

Cross-modality visible-infrared person re-identification (VI-ReID) aims to recognize images with the same identity between visible modality and infrared modality, which is a very challenging task because it not only includes the troubles of variations between cross-cameras in traditional person ReID, but also suffers from the huge differences between two modalities. Some existing VI-ReID methods extract the modality-shared information from global features through single-stream or double-stream networks, while ignoring the complementarity between fine-grained and coarse-grained information. To solve this problem, our paper designs a multi-granularity feature utilization network (MFUN) to make up for the lack of shared features between different modalities by promoting the complementarity between coarse-grained features and fine-grained features. Firstly, in order to learn fine-grained shared features better, we design a local feature constraint module, which uses both hard-mining triplet loss and heterogeneous center loss to constrain local features in the common subspace, so as to better promote intra-class compactness and inter-class differences at the level of sample and class center. Then, our method uses a multi-modality feature aggregation module for global features to fuse the information of two modalities to narrow the modality gap. Through the combination of these two modules, visible and infrared image features can be better fused, thus alleviating the problem of modality discrepancy and supplementing the lack of modality-shared information. Extensive experimental results on RegDB and SYSU-MM01 datasets fully prove that our proposed MFUN outperforms the state-of-the-art solutions. Our code is available at https://github.com/ZhangYinyinzzz/MFUN .
Instant Messaging Application Encrypted Traffic Generation System

Instant Messaging Applications (IMAs) have become the leading communication tool for smartphone users. While it is insightful for network operators and security researchers to monitor and analyze the network traffic of their organization, there is a lack of research on IMA encrypted traffic analysis. In a companion work [1], we introduced a flow-based encrypted IMA traffic analysis using a data driven approach. Given the lack of publicly available data in this area, a new encrypted IMA traffic generation system is designed and implemented to automatically generate and label encrypted IMA traffic including Discord, Facebook Messenger, Signal, Microsoft Teams, Telegram, and WhatsApp. The new system utilizes a combination of open-source tools to emulate user behavior, to capture, filter and label the resulting traffic directly on an Android device. This demonstration shows the functionality of the proposed system via data generation, capture, and analysis of the six IMAs.
What virtualization can do for maintenance: the HSM case

In the automotive sector, one easily looks into two decades of software maintenance at the vehicle level. During this time, either the processor-specific version of the potentially complex operating system (OS) kernel in use has to be patched or completely exchanged.
A Stacking Learning-Based QoE Model for Cloud Gaming

Cloud gaming is a new paradigm that allows more cost-effective gaming for both users and game developers. The market is expected to grow 50-60% annually, reaching 22 billion USD by 2030. Gaming providers and ISPs require models of user satisfaction in order to improve their management of the cloud and network infrastructure. This paper analyses and proposes models that estimate the QoE of cloud gaming. Such models take as features network and game metrics. We assume an information sharing agreement among the cloud gaming platform and the ISP, allowing for a richer dataset. Data collection is performed with real users playing on a realistic testbed using similar protocols of the NVIDIA Geforce Now cloud gaming platform. We use stacking learning in order to improve the accuracy of the models, making a search for the best models and stacking them. We tested various improvements to the models, such as removing users with very low number of matches. Experiments show that models with more experienced players obtained a better precision, achieving 36.08%. When considering a range of plus or minus one within the estimated precision, the hit ratio was 86.56%. We also analyzed the model’ s sensitivity to inputs using feature importance analysis.
Comparative Analysis of 2D Object Detection Algorithms and real-time implementation using RTMAPS

The development of autonomous vehicles has garnered significant attention in recent years due to its potential to revolutionize the way we move and its impact on society, security, and the environment. One of the crucial components of these vehicles is the object detection system, responsible for identifying and localizing objects in the road environment. This task is essential for decision-making processes in autonomous vehicles, such as navigating the vehicle, avoiding obstacles, and changing the driving direction.Despite the challenges posed by the variability of objects in the road environment, this paper proposes a system for autonomous vehicle guidance based on embedded systems and the RTMAPS tool. The primary focus of the system is to perform effective object detection.To achieve this objective, the study evaluates several stateof-the-art algorithms for 2D object detection and selects the best algorithms based on precision and inference time. The selected algorithm is then implemented using RTMAPS in realworld scenarios on the university’s track. The results of this study provide valuable insights for the research and practical community in the field of autonomous vehicles and serve as a reference for future work in object detection
Efficient Periodicity Analysis for Real-Time Anomaly Detection

The analysis of time series data is critical for the effective management of IT infrastructures, with real-time anomaly detection being of particular importance. A crucial component in any anomaly detection pipeline is the characterization of individual data streams so that the most appropriate algorithm can be deployed on a case-by-case basis. Periodicity is a characteristic that directly impacts the choice of algorithm; efficient identification of its presence, or absence, is essential. In the current paper, we develop a periodicity detector based on the Lomb-Scargle periodogram and analyze its performance when exposed to varying quantities of data. Our analysis uses the well-known F1-score to identify the lower limit on the data volume required for maximal detector performance. We then extend our periodicity detection method to include a statistical analysis based on the Wilson score to establish confidence in the detector result. We demonstrate that in the absence of ground-truth labels, our method can quantity the minimum amount of data and the minimum number of trials required to correctly, and definitively, identify periodicity within a data stream.
DeepASTC:Antenna Scan Type Classification Using Deep Learning

In this work, we propose a new method which we call DeepASTC, for antenna scanning type classification in Electronic Warfare Systems. DeepASTC is a deep neural network composed of LSTMs. Amplitude patterns of the deinterleaved radar pulses are fed into our network, and the corresponding scanning type is automatically obtained. DeepASTC and the Multiclass Support Vector Machine (SVM) based classifier method are compared. It is observed that the proposed DeepASTC is able to achieve 93.8% correct classification rate on average, whereas the corresponding rate for the Multiclass SVM method is 86.3%. Conducted experiments show that, the proposed DeepASTC performs successfully on the synthetic data sets.
Poisson Conjugate Prior for PHD Filtering based Track-Before-Detect Strategies in Radar Systems

A variety of filters with track-before-detect (TBD) strategies have been developed and applied to low signal-to-noise ratio (SNR) scenarios, including the probability hypothesis density (PHD) filter. Assumptions of the standard point measure-ment model based on detect-before-track (DBT) strategies are not suitable for the amplitude echo model based on TBD strategies. However, based on different models and unmatched assumptions, the measurement update formulas for DBT-PHD filter are just mechanically applied to existing TBD-PHD filters. In this paper, based on the Kullback-Leibler divergence minimization criterion, finite set statistics theory and rigorous Bayes rule, a principled closed-form solution of TBD-PHD filter is derived. Furthermore, we emphasize that PHD filter is conjugated to the Poisson prior based on TBD strategies. Next, a capping operation is devised to handle the divergence of target number estimation as SNR increases. Moreover, the sequential Monte Carlo implementations of dynamic and amplitude echo models are proposed for the radar system. Finally, Monte Carlo experiments exhibit good performance in Rayleigh noise and low SNR scenarios.
Scanning Radar Scene Reconstruction With Deep Unfolded ISTA Neural Network

Complex scene reconstruction is one of the most critical issues in scanning radar processing. The azimuth echo of the scanning radar can be equivalent to the convolution result of the scene scattering coefficient and the antenna pattern. Iter-ative shrinkage-thresholding algorithm (ISTA) has been proven effective in the target reconstruction of the scanning radar, but it often performs unsatisfactory reconstruction quality on complex scenes. This paper proposes a new learning-based approach, an improved ISTA-based deep unfolding network, to reconstruct the scene information from the scanning radar echoes. Unlike the traditional analysis-based method, we established a deep unfolded scene reconstruction network based on the structure of ISTA. This network can learn the optimal network parameters through the input radar data, which avoids the manual selection of parameters in the traditional method. Besides, we apply a loss function to ensure the effectiveness of the sparse transformation so that the method can recover target information from scanning radar echoes in various complex scenes. Extensive experiments demonstrate that this method can highly improve scene reconstruction performance.
Microwave Quantum Radar using a Josephson Traveling Wave Parametric Amplifier and a Phase-Conjugate Receiver for a long-distance detection

A Microwave Quantum Radar (MQR) based on the Quantum Illumination (QI) protocol and using a Josephson traveling wave parametric amplifier (JTWPA) and a Phase-Coniugate Receiver is introduced for the first time. A JTWPA, operating as a two-mode squeezed vacuum state quantum source, emitting a microwave signal-idler entangled state is developed and experimentally characterised. A phase-conjugate receiver is used as a joint detecting unit for the entangled signal-idler pairs, and its corresponding receiver operating characteristic (ROC) curves are reported and compared to the optimal classic scheme. The proposed MQR scheme paves the way to a long-distance target detection.