Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Trace Side-Channel Attacks on CRYSTALS-Dilithium: Myth or Reality?

We present a side-channel attack on CRYSTALS-Dilithium, a post-quantum secure digital signature scheme, with two variants of post-processing. The side-channel attack exploits information leakage in the secret key unpacking procedure of the signing algorithm to recover the coefficients of the polynomials in the secret key vectors s 1 and s 2 by profiled deep learning-assisted power analysis. In the first variant, one half of the coefficients of s 1 and s 2 is recovered by power analysis and the rest is derived by solving a system of linear equations based on t = As 1 + s 2 , where A and t are parts of the public key. This case assumes knowledge of the least significant bits of the vector t , t 0 . The second variant waives this requirement. However, to succeed, it needs a larger portion of s 1 to be recovered by power analysis. The remainder of s 1 is obtained by lattice reduction. Once the full s 1 is recovered, all the other information necessary for generating valid signatures can be trivially derived from the public key. We evaluate both variants on an ARM Cortex-M4 implementation of Dilithium-2. The profiling stage (trace capture and neural network training) takes less than 10 hours. In the attack assuming that t 0 is known, the probability of successfully recovering the full vector s 1 from a single trace captured from a different from profiling device is non-negligible (9%). The success rate approaches 100% if multiple traces are available for the attack. Our results demonstrate the necessity of protecting the secret key of CRYSTALS-Dilithium from single-trace attacks and call for a reassessment of the role of compression of the public key vector t in the security of CRYSTALS-Dilithium implementations.
Approximating the RSRP with Predictive Maintenance

We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational re-sults for two instances derived from real world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions.
Visual Scene Representation with Hierarchical Equivariant Sparse Coding

We propose a hierarchical neural network architecture for unsupervised learning of equiv-ariant part-whole decompositions of visual scenes. In contrast to the global equivariance of group-equivariant networks, the proposed architecture exhibits equivariance to part-whole transformations throughout the hierarchy, which we term hierarchical equivariance. The model achieves these structured internal representations via hierarchical Bayesian inference, which gives rise to rich bottom-up, top-down, and lateral information ﬂows, hypothesized to underlie the mechanisms of perceptual inference in visual cortex. We demonstrate these useful properties of the model on a simple dataset of scenes with multiple objects under independent rotations and translations.
Efficient Attack Detection with Multi-Latency Neural Models on Heterogeneous Network Devices

To achieve fast and accurate attack detection, some works manually tailor neural networks (NNs) for deployment on CPUs of gateways, routers, or even programmable switches. However, with such solutions, NNs must be custom-tailored across different devices to meet the heterogeneous settings (e.g., OS and CPU types). Even worse, a model may require frequent adjustments to adapt to the same device's varying traffic rates. In this paper, we present Soteria, an automated multi-latency NN generation and scheduling system for fast and accurate detection against fluctuating traffic rates across heterogeneous hardware. Soteria first uses an evolutionary training algorithm to evolve the Pareto front, i.e., the set of NNs with a good spread on accuracy and model size. Then, for each device, Soteria filters the optimal multi-latency NNs by non-dominating sorting on the NNs' test latency on the device. Finally, to cope with the dynamic traffic rate, we design a heuristic scheduling scheme that adaptively selects NN s to maintain a balance between the detection accuracy and latency.
Concurrent Detection of Known Defects and Out-of-Distribution Instances in Building Inspections: Advancements in Deep Classification

Early detection of structural damages is crucial for maintaining a good health level in buildings preventing further deterioration and irreversible harm. Current structural inspections are predominantly carried out by industry professionals, often aided by drone technology to facilitate faster assessments and access hard-to-reach areas. To analyze the data gathered from visual inspections, machine learning-based approaches are commonly employed. While these existing machine learning classifiers excel in identifying the defects they have been trained on, they face limitations in recognizing unseen or unknown data classes.In this study, we propose a novel approach to enhancing the capability of civil structural defect classifiers to classify damages in buildings based on known damage types while also identifying unknown damage categories by incorporating GAN assistance. The proposed GAN-assisted classifier demonstrates significant improvements in detecting Out-Of-Distribution (OOD) data, showing a remarkable enhancement of 15% to 35% compared to the conventional defect classifier. Additionally, we further improved the OOD detection metrics by augmenting the GAN-assisted classifier’s training set with GAN-synthesized data, resulting in an impressive increase of approximately 35-40% in performance. Despite these advancements, the classifier maintains a high classification accuracy of around 96% for known distribution data. The developed approach showcases promising potential for implementation within drones, enabling automated damage detection and classification in buildings, a domain where publicly available data is typically limited.
Random Forest Assisted Differential Evolution for Multi-server Congested p-median Problem

This paper addresses the facility location problem in the context of multiple-server facilities subject to congestion. The objective is to select a subset of facilities from a pool of candidate locations in order to meet customers’ demands. Additionally, the number of servers allocated to each facility is treated as a decision variable, and the service time for each server follows an exponential distribution. As network location problems are known to be NP-hard, this study introduces a random forest as a surrogate model with differential evaluation to minimize the aggregate expected traveling times and aggregate expected waiting times of customers. The proposed algorithm is implemented and evaluated on a set of test problems with different sizes and specifications, demonstrating its high efficiency compared to differential evaluation.
Combining Incompatible Spatial Data

Global positioning systems (GPSs) and geographical information systems (GISs) have been widely used to collect and synthesize spatial data from a variety of sources. New advances in satellite imagery and remote sensing now permit scientists to access spatial data at several different resolutions. The Internet facilitates fast and easy data acquisition. In any one study, several different types of data may be collected at differing scales and resolutions, at different spatial locations, and in different dimensions. Many statistical issues are associated with combining such data for modeling and inference. This article gives an overview of these issues and the approaches for integrating such disparate data, drawing on work from geography, ecology, agriculture, geology, and statistics. Emphasis is on state-of-the-art statistical solutions to this complex and important problem.
Early Risk Prediction of Depression Based on Social Media Posts in Arabic

Depression is a prevalent global health issue, impacting various aspects of individuals’ lives, including home and social interactions. In the Arabic environment, the stigma surrounding mental disorders and the limited awareness in the psychiatry domain has made the early diagnosis of depression a challenging task. However, social media platforms have enabled individuals to express their thoughts and personal experiences, making these platforms a valuable resource for mental health monitoring. In this paper, we propose an approach to predict the early signs of depression utilizing posts expressed in Arabic on the Twitter platform. The proposed methodology integrates knowledge extracted using an LLM-based transformer, the UMLS medical knowledge resource, and machine learning prediction algorithms. To the best of our knowledge, this is the first research study that maps LLM-based translated texts to external medical knowledge resources to improve the accuracy of the prediction model. The proposed model consists of four phases. Firstly, NLP-based data preprocessing pipeline is employed to ensure the input dataset is in a suitable format for analysis. Secondly, the ChatGPT transformer is utilized to translate Arabic tweets into English, enabling further processing and analysis in English. Thirdly, relevant medical concepts are extracted from the translated text using the quickUMLS tool and UMLS metathesaurus, aiding in identifying important terms related to mental health. Fourthly, TF-IDF and Bag of Words (BOW) algorithms are used to assign weights to the extracted features, highlighting the significance of concepts. Finally, classification algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and Stochastic Gradient Descent (SGD), are trained using the extracted concepts. Among these classifiers, Random Forest with Bag of Words demonstrated the best performance, achieving an accuracy of 80.24%.
Enhancing Automatic Speech Recognition Quality with a Second-Stage Speech Enhancement Generative Adversarial Network

Speech enhancement is an essential preprocessing stage for automatic speech recognition in noisy conditions; however, the distortion caused by the denoising process may lead to degradation in automatic speech recognition performance. This paper presents a deep learning-based speech enhancement architecture to overcome this issue by applying a second-stage network that deals with distortion noise. Moreover, a signal-to-noise ratio binary classifier is implemented to activate the speech enhancement network for intrusive noise environments only, which improves the overall performance. The proposed architecture outperforms powerful models in the literature, as it improves a challenging noisy speech test set by 0.8 and 5.9% improvement in the quality and intelligibility scores, respectively. Furthermore, the architecture improves the performance of automatic speech recognition with a 13.8% reduction in the word error rate at 0dB signal-to-noise ratio. Finally, the second-stage network was proven to improve the performance of first-stage speech enhancement models, not previously seen in the training process.
Robust Curvelet-Domain Primary-Multiple Separation with Sparseness Constraints

P226 4250 ROBUST CURVELET-DOMAIN PRIMARY-MULTIPLE Abstract SEPARATION WITH SPARSENESS CONSTRAINTS FELIX J. HERRMANN 1 and DIRK J. VERSCHUUR 2 1 Department of Earth and Ocean Sciences University of British Columbia Canada 2 Faculty of Applied Sciences Delft University of Technology The Netherlands A non-linear primary-multiple separation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for both primaries and multiples. As such curvelets frames are ideal candidates to separate primaries from multiples given inaccurate predictions for these two data components. The method derives its robustness regarding the presence of
Waste Genie: Learning Environmental Sustainability from Waste Sorting and Interactive Feedback

Waste management is the foundation of sustainable living. Recently, researchers and organizations have undertaken a variety of efforts to support sustainability through improved waste management. We designed and studied a web-based educational technology that harnesses environmental sustainability information and resources to support learning. Waste Genie combined the most prevalent formal and informal learning features by utilizing assessment with interactive feedback to facilitate users learning waste sorting. A field trial was deployed to examine the technology’s effectiveness and the learning impacts. The system logs were modeled with Hidden Markov Model to analyze the participants’ sequential patterns. The results showed that the users were confident using the system, and the platform was successful in engaging users through a variety of activities. The participants found the system very easy to use, they expressed a desire to have more social features, such as commenting and social sharing. The study also revealed that Waste Genie had a positive impact on users’ awareness of waste management, with participants demonstrating improvements in their waste sorting behaviors.
Don’t denigrate the CAVE ! A comparative Examination of CAVE and HMD for navigation in virtual worlds

This paper conducts a comprehensive analysis of user experiences by comparing navigation and cybersickness between two distinct categories of immersive devices: CAVE and HMD. Using consistent methodology and analysis for both technologies in the same application to underscores disparities in user experiences, particularly in navigation and exploration tasks, addressing a gap in the existing literature. The study comprises two experiments with differing navigation paradigms. The first demanded active participant navigation in a complex virtual environment, focusing on distinctions like field of view and field of regard intrinsic to CAVE and HMD technologies. Physiological parameters (heart rate and skin conductance) and the Virtual Reality Sickness Questionnaire (VRSQ) were recorded to assess cybersickness levels. Results indicate no significant variations in self-rated cybersickness but a higher heart rate for HMD and longer completion time in the CAVE. Participants favored HMDs personally. In the second experiment, participants were guided through an automated virtual environment (VE) walk, recording similar physiological and psychological measurements. Although no significant inter-device variations emerged in psychological measurements, a notable influence of the HMD on physiological cybersickness data and postural stability was observed. Nevertheless, other measurements and participant feedback did not align with substantial cybersickness. Overall, our results provide a better understanding of the differences between these two VR displays.
The Application of ANFIS to Prediction of the Amount of Emissions from an Emitter Activity to Reduce Climate Change

Understanding the links between C02 emissions from fuel consumption and climate change may assist countries in determining the amount of C02 or C02 equivalent emissions, and act accordingly by reformulating new energy policies to reduce emissions and achieve sustainable development. This understanding can help guide countries in developing better energy policies that are designed to reduce C02 and equivalent emissions, while still promoting economic growth and development. To reach the global climate goals set by the United Nations [25], countries must take decisive action to reduce emissions from fuel consumption and energy production while promoting clean energy, which can help mitigate the effects of climate change. Understanding the magnitude of the emissions problem is necessary before coming up with innovative, long-lasting solutions to better comprehend the issue of emissions. An important first step in achieving this understanding is to accurately assess the current levels of emissions from fuel consumption and energy production in a certain country and analyze the amount of these emissions and finally implement strategies to reduce them. Using machine learning technologies such as predictive analytics, and artificial intelligence can help create meaningful insights into the data to understand patterns in emissions that could enable better strategies for reducing emissions. Using a dataset that belongs to the Canadian government, with input data including engine size, number of cylinders, and fuel consumption, an ANFIS model was created to predict emissions. The amount of emissions represents the output. A variety of membership functions were tested in order to gain the best results. Another dataset was generated by applying the Extra-Trees regressor algorithm to the mentioned dataset and retraining the ANFIS model using the generated dataset to achieve better results. The resulting model can then serve as a trustworthy tool to support the application of efficient mitigation strategies and to inform and help policymakers.
mBBR - Improving BBR Performance Over Rate-Limited Mobile Networks

In spite of the advances in mobile networks, most mobile data plans impose a fixed monthly data quota, beyond which the attainable bandwidth is explicitly limited to a much lower data rate. This rate-limited behavior could degrade TCP performances significantly, as confirmed by a major service provider who observed strong correlation between high packet loss rate and mobile rate limiting. The high packet loss translates directly into increased bandwidth cost which is significant in a large-scale service. This work investigates this problem in two steps. First, we establish the link between the high loss rate observed and mobile network rate limiting through experiments in both controlled testbed and production mobile networks. The results revealed that packet loss can and does increase dramatically in rate-limited mobile networks. This affects both TCP Cubic and BBR, the two most widely deployed TCP implementations. BBR, in particular, was impacted far more significantly, resulting in packet loss rates exceeding 40% in some cases. Second, we analyzed BBR's operations under rate limiting to uncover the causes and developed new mechanisms - mBBR, to improve its performance. Experimental results show that mBBR can reduce BBR's packet loss rate by up to 88%, thereby saving substantial bandwidth costs incurred in retransmitting lost packets when the user is under rate limiting.
Moderating Role of Teacher Characteristics in the Relationship between Accounting Teachers’ Pedagogical Content Knowledge and Lesson Delivery

The purpose of the study was to examine the moderating role of teacher characteristics in the relationship between accounting teachers’ pedagogical content knowledge and lesson delivery. With the use of descriptive research design, the researchers tested three research hypotheses that were formulated from the problem statement. The population of the study consisted of 53 accounting teachers teaching at 11 SHS in the Cape Coast Metropolis. Census approach was used to select all the 53 teachers from the 11 SHS. A questionnaire was adapted from Bosu (2010); Aksu et al., (2014) and were modified to suit the study. Findings of the study indicated that teacher characteristics (gender, age and teacher experience) did not significantly moderate the relationship between teachers’ pedagogical content knowledge and lesson delivery. It was therefore recommended that equal opportunity be given to all teachers irrespective of their gender, age or experience in terms of workshops, in-service training or any
Distributed Fixed-Time Secondary Control for DC Microgrid with Less Information Exchange

DC microgrids are a feasible grid system to host multiple distributed generators (DGs). In terms of the control and coordination of hosted DGs, it is required that the integrated secondary control scheme of DC microgrids can achieve fast dynamics. To fulfill this target, a novel fixed-time secondary control scheme is put forward in this paper to achieve a concurrent DC bus voltage regulation and current sharing among DGs within a fixed time. Notably, the proposed method only needs to transmit one variable to the neighboring DGs, which can considerably reduce the communication traffic compared to the existing latest method. Moreover, this control scheme need not sample the DC bus voltage, benefiting an easier implementation and improved system reliability. The stability of the proposed scheme is theoretically proved. The results of studied cases are provided to demonstrate the effectiveness of the proposed strategy.
A Deep Learning Speech Enhancement Architecture Optimised for Speech Recognition and Hearing Aids

With the fast progression of the speech enhancement field after the introduction of deep learning techniques, there is a need to consider the adjustments needed to employ these techniques for real-life applications. In this work, we present an optimised deep learning speech enhancement architecture for automatic speech recognition and hearing aids, two key speech enhancement applications. A speech enhancement architecture with a signal-to-noise ratio switch is presented for automatic speech recognition systems, to avoid denoising artifacts that cause performance degradation in the case of clean or high signal-to-noise speech. Moreover, a smart speech enhancement architecture is presented for hearing aids to retain important emergency noise in the audio signal. The presented work achieved 13.9% reduction in the word error rate of an automatic speech recognition system. Additionally, the smart speech enhancement architecture resulted in 0.18 improvement in HAAQI audio quality metric.
Target Voltage Iterations Based Global Flexible Power Point Tracking Algorithm Under Partial Shading Photovoltaic Systems

Global flexible power point tracking (GFPPT) is proposed to realize the flexible power point tracking (FPPT) of photovoltaic systems under partial shading conditions (PSC). Because the working process of GFPPT is similar to global power point tracking (GMPPT), the performance of GFPPT can be improved by applying the existing advanced GMPPT algorithm. However, the improvement of GFPPT is limited while applying the existing advanced GMPPT algorithm directly. So, a target-voltage-iterations (TVI) based GFPPT algorithm is proposed in this article to further increase the performance of GFPPT during PSC. Compared with the advanced GFPPT algorithms, the proposed TVI-based GFPPT algorithm shows the advantages of fast-tracking speed and little energy difference during the tracking by skipping the voltage section without the reference power. Followed by theoretical analysis, the simulation and experimental evaluation validate the claimed advantages of the proposed TVI-based GFPPT algorithm.
On the Anomaly of Balmer Line Profiles of A-Type Stars. Fundamental binary systems

In previous work, Gardiner et al. (1999) found evidence for a discrepancy between the T e f f obtained from Balmer lines with that from photometry and fundamental values for A-type stars. An investigation into this anomaly is presented using Balmer line profiles of stars in binary system with fundamental values of both T e f f and log g. A revision of the fundamental parameters for binary systems given by Smalley & Dworetsky (1995) is also presented. The T e f f obtained by fitting Hα and Hβ line profiles is compared to the fundamental values and those obtained from uvby photometry. We find that the discrepancy found by Gardiner et al. (1999) for stars in the range 7000 K ≤ T e f f ≤ 9000 K is no longer evident.