Miracle-dz/UniFAR-FTUs huggingface dataset:
Denial of Service Attacks Detection in Software-Defined Wireless Sensor Networks

Software-defined networking (SDN) is a promising technology to overcome many challenges in wireless sensor networks (WSN), particularly with respect to flexibility and reuse. Conversely, the centralization and the planes' separation turn SDNs vulnerable to new security threats in the general context of distributed denial of service (DDoS) attacks. Stateof-the-art approaches to identify DDoS do not always take into consideration restrictions in typical WSNs e.g., computational complexity and power constraints, while further performance improvement is always a target. The objective of this work is to propose a lightweight but very efficient DDoS attack detection approach using change point analysis. Our approach has a high detection rate and linear complexity, so that it is suitable for WSNs. We demonstrate the performance of our detector in software-defined WSNs of 36 and 100 nodes with varying attack intensity (the number of attackers ranges from 5% to 20% of nodes). We use change point detectors to monitor anomalies in two metrics: the data packets delivery rate and the control packets overhead. Our results show that with increasing intensity of attack, our approach can achieve a detection rate close to 100% and that the type of attack can also be inferred.
Simulation of the traveling wave burning on epithermal neutrons on the year time scale.

We present the newly obtained results of two computer simulations of the epithermal neutron-nuclear burning in natural uranium. Each of them modeled the period of six months of the traveling wave reactor (TWR) operation -- for two different flux densities of an external neutron source. The simulation results confirm the existence of a nuclear burning wave at longer time scales, and reveal the dependence of the wave burning modes on the parameters of an external neutron source.
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding

Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their applicability in practical machine learning problems. The deep neural network-to-SNN conversion approach has been widely studied to broaden the applicability of SNNs. Most previous studies, however, have not fully utilized spatio-temporal aspects of SNNs, which has led to inefficiency in terms of number of spikes and inference latency. In this paper, we present T2FSNN, which introduces the concept of time-to-first-spike coding into deep SNNs using the kernel-based dynamic threshold and dendrite to overcome the aforementioned drawback. In addition, we propose gradient-based optimization and early firing methods to further increase the efficiency of the T2FSNN. According to our results, the proposed methods can reduce inference latency and number of spikes to 22% and less than 1%, compared to those of burst coding, which is the state-of-the-art result on the CIFAR-100.
Finnish Language Modeling with Deep Transformer Models

Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time. In this project, we investigate the performance of the Transformer architectures-BERT and Transformer-XL for the language modeling task. We use a sub-word model setting with the Finnish language and compare it to the previous State of the art (SOTA) LSTM model. BERT achieves a pseudo-perplexity score of 14.5, which is the first such measure achieved as far as we know. Transformer-XL improves upon the perplexity score to 73.58 which is 27\% better than the LSTM model.
Probabilistic hysteresis in an isolated quantum system: The microscopic onset of irreversibility from a quantum perspective

\emph{Probabilistic hysteresis} is a manifestation of irreversibility in a small, isolated classical system [Sci. Rep. 9, 14169]: after a slow cyclic sweep of a control parameter, the probability that a microcanonical ensemble returns to the neighborhood of its initial energy is significantly below one. A similar phenomenon has recently been confirmed in a corresponding quantum system for not too small particle number $N$. Quantum-classical correspondence has been found to be non-trivial in this case, however; the rate at which the control parameter changes must not be extremely slow and the initial distribution of energies must not be too narrow. In this paper we directly compare the quantum and classical forms of probabilistic hysteresis by making use of the Husimi quantum phase space formalism. In particular we demonstrate that the classical ergodization mechanism, which is a key ingredient in classical probabilistic hysteresis, can lead to a breakdown of quantum-classical correspondence rather than to quantum ergodization. As a result strong quantum effects in the long-term evolution are present, even though the quantum corrections in the equations of motion are proportional to $1/N$ and therefore would naively seem to be small. We also show, however, that quantum ergodization is restored by averaging over energies, so that for sufficient initial energy width and not-too-slow sweep rate the classical results are recovered after all. Finally we show that the formal incommutability of the classical and adiabatic limits in our system, leading to the breakdown of quantum-classical correspondence in the quasi-static limit, is due to macroscopic quantum tunneling through a large energetic barrier. This explains the extremely slow sweep rates needed to reach the quantum adiabatic limit that were reported in our previous work.
Chemical abundance analysis of extremely metal-poor stars in the Sextans dwarf spheroidal galaxy

Context.Metal-poor components of dwarf galaxies around the Milky Way could be remnants of the building blocks of the Galactic halo structure. Low-mass stars that are currently observed as metal-poor stars are expected to have formed in chemically homogeneous clusters in the early phases of galaxy formation. They should have already disintegrated and should exhibit large scatter in abundance ratios of some sets of elements (e.g., Sr/Ba) in the Milky Way field stars. However, chemical abundance ratios are expected to cluster in very metal-poor stars in dwarf galaxies because the number of clusters formed in individual galaxies in the very early phase is expected to be quite limited.Aims. We examine the possible clustering of abundance ratios of Sr and Ba in the Sextans dwarf galaxy to test for the clustering star formation scenario.Methods. We investigate a total of 11 elements (C, Mg, Ca, Sc, Ti, Cr, Mn, Ni, Zn, Sr, Ba) in five stars in the Sextans dwarf galaxy. Previous studies suggest that these have similar abundance ratios. In this study, we focus on the abundance ratio of Sr to Ba. The observations are based on high-resolution spectroscopy (R= 40 000) using the Subaru Telescope High Dispersion Spectrograph.Results. The distribution ofα/Fe abundance ratios of the Sextans dwarf galaxy stars is slightly lower than the average of the values of stars in the Galactic halo. The Sr/Ba abundance ratios for the five metal-poor stars are in good agreement, and this clumping is distinctive compared to the [Sr/Ba] spread seen in the metal-poor halo stars. We find that the probability of such clumping is very small if the Sextans stars have distributions of Sr and Ba abundances similar to halo stars.Conclusions. In the Sextans dwarf galaxy, five out of six of the extremely metal-poor stars for which abundance ratios are well studied so far show clear clustering in abundance ratios including Sr/Ba. These observations tend to support the hypothesis that these stars were formed from a cloud of homogeneous chemical composition.
ACADEMIC STRESS AND TIME MANAGEMENT SKILLS A CORRELATED STUDY

The present study was designed to assess and compare Academic Stress and Time Management Skills of male and female students of 10+2 of Science stream of Haryana state, preparing for entrance exams to get admission in professional colleges for medical and engineering courses. The sample of the study comprised of 410 adolescents with equal number of males and females (n=205). The range of the subjects was 16-18years. The sample was chosen by random sampling procedure. Bisht Battery Scale of Academic Stress (BBSS) developed by Dr. Asha Rani Bisht (2005) and Time Management Scale designed by D.N. Sansanwal and MeenakshiPrashar (2007) was administered on the sample. It was hypothesized that female students tend to be high on Academic Stress than male students. It was hypothesized that female students tend to be high on time management skills than male students. It was also hypothesized that Academic stress is negatively correlate with Time management skills. The obtained data was analyzed by employing mean, standard deviation,‘t’-test and pearson’s ‘r’. Significant difference was found in Academic Stress and Time Management Skills of male and female students of 10+2 of Science stream. Female students were found high on academic stress compare to male students. Female students were also found high on Time Manage ment Skills compare to male students. There is a negative and significant relationship was found between Academic Stress and Time Management Skill.
University of Groningen Altered Gut Microbial Fermentation and Colonization with Methanobrevibacter smithii in Renal Transplant Recipients Knobbe,

Renal transplant recipients (RTRs) often suffer from posttransplant diarrhea. The observed dysbiosis in RTR may influence the fermentation processes in the gut. In this study, we aimed to investigate whether fermentation differs between RTRs and healthy controls (HCs), by measuring breath H2 and CH4 concentrations. Additionally, we determined the fecal presence of the methanogen Methanobrevibacter smithii (M. smithii), which plays a main role in the process of methanogenesis. Data from the TransplantLines Biobank and Cohort Study (NCT03272841) was used. A total of 142 RTRs and 77 HCs were included. Breath H2 concentrations in RTRs were not significantly different from HCs. Breath CH4 concentrations in RTRs were significantly lower compared with HCs (median [interquartile range (IQR)] 7.5 [3.9–10.6] ppm vs. 16.0 [8.0–45.5] ppm, p < 0.001). M. smithii was less frequently present in the feces of RTRs compared to HCs (28.6% vs. 86.4% resp., p < 0.001). Our findings regarding the altered methanogenesis in the gut of RTRs show similarities with previous results in inflammatory bowel disease patients. These findings provide novel insight into the alterations of fermentation after renal transplantation, which may contribute to understanding the occurrence of posttransplant diarrhea.
Efficacy of Combination of Meropenem with Gentamicin, and Amikacin against Resistant E. coli Isolated from Patients with UTIs: in vitro Study

Seventy five E. coli isolates were collected from urine of patients with urinary tract infections in AL-Kadhimia and AL-Yarmook teaching hospitals in Baghdad for a period between 22/11/2009 to 15/3/2010, from these samples twenty five isolates were selected according to their pattern of the highest resistance as these showing multi-drug resistances and tested to specify their minimum inhibitory concentration for (meropenem, gentamicin and amikacin), meropenem was found having the lowest MIC comparing with others. This study also includes in vitro effects of various combinations of three types of antimicrobials (meropenem, gentamicin and amikacin) against twenty five E. coli isolates.Among combinations the combination of meropenem with the other types of antimicrobials showed high synergistic effect when 1/4+1/4 MIC for each antimicrobial were used. While combinations of amikacin with gentamicin in some isolates showed additive effect when 1/2+1/2 MIC for each antimicrobial were used. The plasmid profile for the twenty five E. coli isolates were studied using Pure Yeild ™ plasmid Miniprep system- Cat. # A1220 – Promega- USA. In order to determined the presence of plasmid for antimicrobials resistance.
Learning to Correct Overexposed and Underexposed Photos

Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting a range of exposure values with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors.
Blind Image Deblurring Using Patch-Wise Minimal Pixels Regularization

Blind image deblurring is a long standing challenging problem in image processing and low-level vision. Recently, sophisticated priors such as dark channel prior, extreme channel prior, and local maximum gradient prior, have shown promising effeciveness. However, these methods are computationally expensive. Meanwhile, since these priors involved subproblems cannot be solve explicitly, non-rigorous approximation is commonly used, which limits the best exploitation of their capability. To address these problems, this work firstly proposes a simplified sparsity prior of local minimal pixels, namely patch-wise minimal pixels (PMP). The PMP of clear images is much more sparse than that of blurred ones, and hence is very effective in discriminating between clear and blurred images. Then, a novel algorithm is designed to efficiently exploit the sparsity of PMP in deblurring. The new algorithm flexibly imposes sparsity promotion on the PMP under the MAP framework, which avoids non-rigorous approximation in existing algorithms while being more computationally efficient. Extensive experiments demonstrate that the proposed algorithm can achieve state-of-the-art performance on both natural and specific images. In terms of both deblurring quality and computational efficiency, the new algorithm is superior to state-of-the-art methods. Code for reproducing the results of the new method is available at https://github.com/FWen/deblur-pmp.git.
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks

This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some equivalence of the graph convolution process regardless it is designed in the spatial or the spectral domain. The obtained general framework allows to lead a spectral analysis of the most popular ConvGNNs, explaining their performance and showing their limits. Moreover, the proposed framework is used to design new convolutions in spectral domain with a custom frequency profile while applying them in the spatial domain. We also propose a generalization of the depthwise separable convolution framework for graph convolutional networks, what allows to decrease the total number of trainable parameters by keeping the capacity of the model. To the best of our knowledge, such a framework has never been used in the GNNs literature. Our proposals are evaluated on both transductive and inductive graph learning problems. Obtained results show the relevance of the proposed method and provide one of the first experimental evidence of transferability of spectral filter coefficients from one graph to another.
Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.
Revisiting T-Norms for Type-2 Fuzzy Sets

Let $\mathbf{L}$ be the set of all normal and convex functions from ${[0, 1]}$ to ${[0, 1]}$. This paper proves that ${t}$-norm in the sense of Walker-and-Walker is strictly stronger that ${t_r}$-norm on $\mathbf{L}$, which is strictly stronger than ${t}$-norm on $\mathbf{L}$. Furthermore, let ${\curlywedge}$ and ${\curlyvee}$ be special convolution operations defined by $$ {(f\curlywedge g)(x)=\sup\left\{f(y)\star g(z): y\vartriangle z=x\right\},} $$ $$ {(f\curlyvee g)(x)=\sup\left\{f(y)\star g(z): y\ \triangledown\ z=x\right\},} $$ for ${f, g\in Map([0, 1], [0, 1])}$, where ${\vartriangle}$ and ${\triangledown}$ are respectively a ${t}$-norm and a ${t}$-conorm on ${[0, 1]}$ (not necessarily continuous), and ${\star}$ is a binary operation on ${[0, 1]}$. Then, it is proved that if the binary operation ${\curlywedge}$ is a ${t_r}$-norm (resp., ${\curlyvee}$ is a ${t_r}$-conorm), then ${\vartriangle}$ is a continuous ${t}$-norm (resp., ${\triangledown}$ is a continuous ${t}$-conorm) on ${[0, 1]}$, and ${\star}$ is a ${t}$-norm on ${[0, 1]}$.
Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images

Background: Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of affected patients increase rapidly, manual severity assessment becomes a labor-intensive task, and may lead to delayed treatment. Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model. Materials and Method: Chest CT images of 176 patients (age 45.3$\pm$16.5 years, 96 male and 80 female) with confirmed COVID-19 are used, from which 63 quantitative features, e.g., the infection volume/ratio of the whole lung and the volume of ground-glass opacity (GGO) regions, are calculated. A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features. Importance of each quantitative feature, which reflects the correlation to the severity of COVID-19, is calculated from the RF model. Results: Using three-fold cross validation, the RF model shows promising results, i.e., 0.933 of true positive rate, 0.745 of true negative rate, 0.875 of accuracy, and 0.91 of area under receiver operating characteristic curve (AUC). The resulting importance of quantitative features shows that the volume and its ratio (with respect to the whole lung volume) of ground glass opacity (GGO) regions are highly related to the severity of COVID-19, and the quantitative features calculated from the right lung are more related to the severity assessment than those of the left lung. Conclusion: The RF based model can achieve automatic severity assessment (non-severe or severe) of COVID-19 infection, and the performance is promising. Several quantitative features, which have the potential to reflect the severity of COVID-19, were revealed.
Multi-Label Text Classification using Attention-based Graph Neural Network

In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. Existing methods tend to ignore the relationship among labels. In this paper, a graph attention network-based model is proposed to capture the attentive dependency structure among the labels. The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task. The generated classifiers are applied to sentence feature vectors obtained from the text feature extraction network (BiLSTM) to enable end-to-end training. Attention allows the system to assign different weights to neighbor nodes per label, thus allowing it to learn the dependencies among labels implicitly. The results of the proposed model are validated on five real-world MLTC datasets. The proposed model achieves similar or better performance compared to the previous state-of-the-art models.
Bounded Estimation Over Finite-State Channels: Relating Topological Entropy and Zero-Error Capacity

We investigate state estimation of linear systems over channels having a finite state not known by the transmitter or receiver. We show that similar to memoryless channels, zero-error capacity is the right figure of merit for achieving bounded estimation errors. We then consider finite-state, worst-case versions of the common erasure, and additive noise channels models, in which the noise is governed by a finite-state machine without any statistical structure. Upper and lower bounds on their zero-error capacities are derived, revealing a connection with the topological entropy of the channel dynamics. Separate necessary and sufficient conditions for bounded linear state estimation errors via such channels are obtained. These estimation conditions bring together the topological entropies of the linear system and the discrete channel.
Patch Quality and Diversity of Invariant-Guided Search-Based Program Repair

Most automatic program repair techniques rely on test cases to specify correct program behavior. Due to test cases' frequently incomplete coverage of desired behavior, however, patches often overfit and fail to generalize to broader requirements. Moreover, in the absence of perfectly correct outputs, methods to ensure higher patch quality, such as merging together several patches or a human evaluating patch recommendations, benefit from having access to a diverse set of patches, making patch diversity a potentially useful trait. We evaluate the correctness and diversity of patches generated by GenProg and an invariant-based diversity-enhancing extension described in our prior work. We find no evidence that promoting diversity changes the correctness of patches in a positive or negative direction. Using invariant- and test case generation-driven metrics for measuring semantic diversity, we find no observed semantic differences between patches for most bugs, regardless of the repair technique used.