Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Classification of Attacks against the Cyber-Physical Security of Smart Grids

Modern critical infrastructures such as Smart Grids (SGs) rely heavily on Information and Communication Technology (ICT) systems to monitor and control operations and states within large-scale facilities. The potential offered by SGs includes an effective integration of renewables, a demand-response action and a dynamic pricing system. The increasing use of ICT for the communication infrastructure of modern power systems offers advantages but can give rise to cyber attacks that compromise the security of the SG. To deal efficiently with the security concerns of SGs, a survey of the different attacks that consider the physical as well as the cyber characteristics of modern power grids is required. In the present paper, first the specific differences between SGs with respect to both Information Technology (IT) systems and conventional energy grids are discussed. Thereafter, the specific security requirements of SGs are presented in order to raise awareness of the new security challenges. Finally, a new classification of cyber attacks, based on the architecture of the SG, is proposed and details for each category are provided. The new classification is distinguished by its focus on the cyber-physical security of the SG in particular, which gives a comprehensive overview of the different threats. Thus, this new classification forms the necessary knowledge-basis for the design of respective countermeasures.
TNF-α elicits phenotypic and functional alterations of vascular smooth muscle cells by miR-155-5p–dependent down-regulation of cGMP-dependent kinase 1

cGMP-dependent protein kinase 1 (PKG1) plays an important role in nitric oxide (NO)/cGMP–mediated maintenance of vascular smooth muscle cell (VSMC) phenotype and vasorelaxation. Inflammatory cytokines, including tumor necrosis factor-α (TNFα), have long been understood to mediate several inflammatory vascular diseases. However, the underlying mechanism of TNFα-dependent inflammatory vascular disease is unclear. Here, we found that TNFα treatment decreased PKG1 expression in cultured VSMCs, which correlated with NF-κB–dependent biogenesis of miR-155-5p that targeted the 3′-UTR of PKG1 mRNA. TNFα induced VSMC phenotypic switching from a contractile to a synthetic state through the down-regulation of VSMC marker genes, suppression of actin polymerization, alteration of cell morphology, and elevation of cell proliferation and migration. All of these events were blocked by treatment with an inhibitor of miR-155-5p or PKG1, whereas transfection with miR-155-5p mimic or PKG1 siRNA promoted phenotypic modulation, similar to the response to TNFα. In addition, TNFα-induced miR-155-5p inhibited the vasorelaxant response of de-endothelialized mouse aortic vessels to 8-Br-cGMP by suppressing phosphorylation of myosin phosphatase and myosin light chain, both of which are downstream signal modulators of PKG1. Moreover, TNFα-induced VSMC phenotypic alteration and vasodilatory dysfunction were blocked by NF-κB inhibition. These results suggest that TNFα impairs NO/cGMP-mediated maintenance of the VSMC contractile phenotype and vascular relaxation by down-regulating PKG1 through NF-κB–dependent biogenesis of miR-155-5p. Thus, the NF-κB/miR-155-5p/PKG1 axis may be crucial in the pathogenesis of inflammatory vascular diseases, such as atherosclerotic intimal hyperplasia and preeclamptic hypertension.
Lactobacillus sakei WIKIM30 Ameliorates Atopic Dermatitis-Like Skin Lesions by Inducing Regulatory T Cells and Altering Gut Microbiota Structure in Mice

Lactobacillus sakei WIKIM30 is a Gram-positive facultative anaerobic bacterium isolated from kimchi, a Korean fermented vegetable food. In this study, we found that WIKIM30 promoted regulatory T cell (Treg) differentiation by inducing dendritic cells with tolerogenic properties. The production of the T helper (Th) 2-associated cytokine interleukin (IL)-4 was decreased, but that of the Treg-associated cytokine IL-10 was increased in splenocytes from ovalbumin-sensitized mice treated with WIKIM30. We also investigated the inhibitory capacity of WIKIM30 on the development of 2,4-dinitrochlorobenzene-induced atopic dermatitis (AD), a Th2-dominant allergic disease in mice. Oral administration of L. sakei WIKIM30 significantly reduced AD-like skin lesions and serum immunoglobulin E and IL-4 levels while decreasing the number of CD4+ T cells and B cells and the levels of Th2 cytokines (IL-4, IL-5, and IL-13) in peripheral lymph nodes and enhancing Treg differentiation and IL-10 secretion in mesenteric lymph nodes. In addition, WIKIM30 modulated gut microbiome profiles that were altered in AD mice, which showed increases in Arthromitus and Ralstonia and a decrease in Ruminococcus abundance. These changes were reversed by WIKIM30 treatment. Notably, the increase in Ruminococcus was highly correlated with Treg-related responses and may contribute to the alleviation of AD responses. Together, these results suggest that oral administration of L. sakei WIKIM30 modulates allergic Th2 responses enhancing Treg generation and increases the relative abundance of intestinal bacteria that are positively related to Treg generation, and therefore has therapeutic potential for the treatment of AD.
Extraction of Symbolic Rules from Artificial Neural Networks

Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, such as breast cancer, iris, diabetes, and season classification problems, demonstrate the effectiveness of the proposed approach with good generalization ability.
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging

Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. However, they often still rely on correct token boundaries. In this paper, we propose to eliminate the need for tokenizers with an end-to-end character-level semi-Markov conditional random field. It uses neural networks for its character and segment representations. We demonstrate its effectiveness in multilingual settings and when token boundaries are noisy: It matches state-of-the-art part-of-speech taggers for various languages and significantly outperforms them on a noisy English version of a benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis. lmu.de/semiCRF.
Two-Person Interaction Action Recognition Based on Multi-Source Information Fusion Algorithm

The existing methods of two-person interaction action recognition based on RGB image is greatly affected by illumination change, object occlusion and environmental change. Considering the respective advantages of the RGB image and the depth image, and the characteristics of information complementarity, this paper proposed a multi - source information fusion algorithm. In our proposed method, the recognition probability of the RGB image and the depth image are weighted fused for the two-person interaction action recognition. Firstly, the frame difference method and ViBe algorithm are respectively used for moving object detection and segmentation. Secondly, histogram of oriented gradient (HOG) features are respectively extracted from the moving regions of the RGB image and the depth image. Thirdly, the nearest neighbor classifier algorithm is used to recognize the actions of the RGB image and the depth image. Finally, the recognition results of the RGB image and the depth image are weighted fused. Experimental results show that the method achieves the better recognition rate.
An Improved Bridgeless SEPIC Converter Without Circulating Losses and Input-Voltage Sensing

In order to overcome the drawbacks of conventional bridgeless single-ended primary inductance converter (SEPIC) power factor correction (PFC) circuits, an improved bridgeless SEPIC PFC circuit is introduced by adding an additional output diode and an inductor. The undesired capacitive coupling loop and the circulating loss are removed. Thus, the high efficiency can be achieved. The operation principle and design consideration of the proposed converter are introduced in detail. Furthermore, a novel control method is employed to control the current continuous mode bridgeless SEPIC PFC circuit without input-voltage sensing. Meanwhile, the UCC28019-based modified hardware implementation circuit is designed and described. A 100-W prototype with 90–135 Vrms input line voltage and 60-V output voltage is built and tested. The corresponding experiment shows the validity of the proposed circuit topology and control strategy. A peak efficiency of 94% and power factor above 0.99 are achieved under 65-kHz switching frequency operation condition.
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization

In this paper we study the limitations of parallelization in convex optimization. A convenient approach to study parallelization is through the prism of \emph{adaptivity} which is an information theoretic measure of the parallel runtime of an algorithm [BS18]. Informally, adaptivity is the number of sequential rounds an algorithm needs to make when it can execute polynomially-many queries in parallel at every round. For combinatorial optimization with black-box oracle access, the study of adaptivity has recently led to exponential accelerations in parallel runtime and the natural question is whether dramatic accelerations are achievable for convex optimization. For the problem of minimizing a non-smooth convex function $f:[0,1]^n\to \mathbb{R}$ over the unit Euclidean ball, we give a tight lower bound that shows that even when $\texttt{poly}(n)$ queries can be executed in parallel, there is no randomized algorithm with $\tilde{o}(n^{1/3})$ rounds of adaptivity that has convergence rate that is better than those achievable with a one-query-per-round algorithm. A similar lower bound was obtained by Nemirovski [Nem94], however that result holds for the $\ell_{\infty}$-setting instead of $\ell_2$. In addition, we also show a tight lower bound that holds for Lipschitz and strongly convex functions. At the time of writing this manuscript we were not aware of Nemirovski's result. The construction we use is similar to the one in [Nem94], though our analysis is different. Due to the close relationship between this work and [Nem94], we view the research contribution of this manuscript limited and it should serve as an instructful approach to understanding lower bounds for parallel optimization.
Effectiveness of reminder strategies on cancer screening adherence: a randomised controlled trial.

BACKGROUND Few randomised controlled trials (RCTs) have evaluated the different recalling approaches for enhancing adherence to faecal immunochemical test (FIT)-based screening. AIM The authors evaluated the effectiveness of two telecommunication strategies on improving adherence to yearly FIT screening. DESIGN AND SETTING A randomised, parallel group trial was performed in a primary care screening practice. METHOD The authors recruited 629 asymptomatic individuals aged 40-70 years with a negative FIT in 2015 to a population-based screening programme. On participation, they were invited to repeat their second round of FIT in 2016, 12 months after the first test. Each participant was randomly assigned to either interactive telephone reminder (n = 207), short message service reminder (SMS, n = 212), or control, where no additional interventions were delivered after the findings of their first FIT was communicated to the participants (n = 210). Reminders in the intervention groups were delivered 1 month before subjects' expected return. Additional telephone reminders were delivered 2 months after the expected return date to all subjects who defaulted specimen return. The outcomes included rates of FIT collection and specimen return up to 6 months after their expected return. RESULTS At 6 months, the cumulative FIT collection rate was 95.1%, 90.4%, and 86.5%, respectively, for the telephone, SMS, and control groups (P = 0.010). The corresponding specimen return rate was 94.1%, 90.0%, and 86.0% (P = 0.022). When compared with the control, only subjects in the telephone group were significantly more likely to collect FIT tubes (adjusted odds ratio [AOR] 3.18, 95% confidence interval [CI] = 1.50 to 6.75, P = 0.003) and return completed specimens (AOR = 2.73, 95% CI = 1.35 to 5.53, P = 0.005). CONCLUSION Interactive telephone reminders are effective at securing previously screened subjects to repeat screening 1 year after a negative finding.
An Optimized Architecture For Decomposed Convolutional Neural Networks

Convolutional neural networks (CNNs) have found extensive applications in various tasks. However, the state-of-the-art CNNs are both computation-intensive and memory-intensive, which brings tremendous hardware implementation challenges. Various methods have been proposed to reduce the model size and computation complexity of a CNN. Among them, when hardware implementation is considered, the Canonical Polyadic decomposition (CPD) method is more suitable due to the regularity in the decomposed filters. Moreover, the CPD method can be combined with widely used pruning methods to compress the model in further. In this paper, to the best of our knowledge, an efficient hardware architecture for CPD-CNNs is proposed for the first time based on a carefully designed data flow. In detail, a reconfigurable fast convolution unit is introduced to reduce the number of multiplications while handling some commonly-used convolution core operations. The proposed architecture is coded with RTL and synthesized under the TSMC 90nm CMOS technology. Our design achieves an equivalent throughput of more than 3TOP/s under 650MHz clock frequency.
CYBERLEGs: un nuovo Sistema robotico per gli amputati di arto inferiore

La perdita totale o parziale di un arto inferiore è una condizione fortemente disabilitante che compromette la salute ed il benessere di molte persone nel mondo. Ci sono differenti ragioni che possono portare ad un’amputazione dell’arto inferiore, quali il diabete, le malattie vascolari, eventi traumatici, tumori, o malformazioni congenite. Il numero crescente di amputazioni di arto inferiore rappresenta oggi una sfida per il sistema sanitario di molti paesi e nuove strategie si rendono necessarie per ridurne l’incidenza, e per mitigare l’impatto sulla salute e la qualità della vita degli amputati, e la sostenibilità dei costi del sistema di assistenza (Ephraim et al., 2003). L’incidenza della totalità delle amputazioni di arto inferiore cambia molto tra i vari paesi: ad esempio, mentre in Giappone l’incidenza (molto bassa) è di 0.4 casi all’anno per 10,000 abitanti, nel Regno Unito i casi annui sono 2 per 10,000 abitanti, ed in alcune comunità degli Stati Uniti (es.: la popolazione maschile di nativi americani della regione dei Navajo) si raggiungono valori (molto alti) di 10 casi annui per 10,000 abitanti (Ephraim et al., 2003). Per avere una migliore stima della dimensione del problema, occorre immaginare che solo negli Stati Uniti ogni anno vi sono 150,000 nuove amputazioni di arto inferiore causate da patologie vascolari1.
High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear input-output dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features.
Clean cloth inputs: removing character self-intersections with volume simulation

Simulation artists frequently work with characters that self-intersect. When these characters are sent as inputs to a cloth simulator, the results can often contain terrible artifacts that must addressed by tediously sculpting either the input characters or the output cloth. In this talk, we apply volume simulation to character meshes and remove self-intersections before they are sent to the cloth simulator. The technique has successfully dealt with very challenging animation scenarios in a production setting, and was applied to all the characters on the short film, Bao.
Ultrahigh-Resolution Optical Vector Analysis for Arbitrary Responses Using Low-Frequency Detection

We propose and experimentally demonstrate an ultrahigh-resolution optical vector analyzer (OVA) for arbitrary responses measurement by using microwave photonic frequency down-conversion and fixed low-frequency detection. It avoids high-speed photodetection and ultrawideband electrical phase-magnitude detection, which are the key challenges to the previous OVAs based on microwave photonics (MWPs). Additionally, the proposed OVA has the capability of measuring arbitrary spectral responses. A high-resolution optical frequency-swept signal generated by using MWP-based techniques is separated into two parts. One portion propagates through an optical device under test (DUT) and then directs to a photodetector (PD), which carries out the spectral responses. In the other branch, the optical signal is accurately shifted by a small frequency and then sent to the PD. By square-law detection, the spectral responses of the optical DUT are thus transferred to a low-frequency photocurrent with a frequency equaling to the shifted frequency. Thus, the spectral responses can be obtained by detecting the photocurrent using a low-frequency electrical phase-magnitude detector working at a fixed frequency. In the experiment, a measurement system is established, by which the magnitude and phase responses of optical devices, such as an optical Hilbert transformer and an optical bandpass filter, are successfully measured. The measurement results are verified by the previous MWP-based OVAs and a commercial instrument.
COBOL to Java and Newspapers Still Get Delivered

This paper is an experience report on migrating an American newspaper company's business-critical IBM mainframe application to Linux servers by automatically translating the application's source code from COBOL to Java and converting the mainframe data store from VSAM KSDS files to an Oracle relational database. The mainframe application had supported daily home delivery of the newspaper since 1979. It was in need of modernization in order to increase interoperability and enable future convergence with newer enterprise systems as well as to reduce operating costs. Testing the modernized application proved to be the most vexing area of work. This paper explains the process that was employed to test functional equivalence between the legacy and modernized applications, the main testing challenges, and lessons learned after having operated and maintained the modernized application in production over the last eight months. The goal of delivering a functionally equivalent system was achieved, but problems remained to be solved related to new feature development, business domain knowledge transfer, and recruiting new software engineers to work on the modernized application.
Module System Monitoring and Fault Detection for Modular and Reconfigurable Robot

A distributed module system monitoring and fault detection of the overall joint for modular and reconfigurable robot (MRR) is proposed in this paper. In the design procedure, the joint commanded torque generated by a distributed fault-tolerant controller is filtered and compared with a filtered torque estimate derived from the nonlinear dynamic model of MRR based on joint torque sensing. Considering the motor output capacity, a novel performance index for providing the operation health conditions of an MRR joint is constructed by using the torque prediction error. The proposed scheme is shown to be able to monitor the health of the overall joint for MRR with any movement, which is essential for robots working in human environments. The presented method has been experimentally tested, and the experimental results have demonstrated the effectiveness of the proposed method.
Supply-Chain Security Enhancement by Chaotic Wireless Chip-Package-Board Interactive PUF

This paper presents a novel assembly-circuit boundary solution for semiconductor supply-chain security enhancement. Chip-Package-Board (CPB) interactive PUF is introduced to incorporates a history of CPB process/assembly variations into a device PUF ID generation. An incremental update of PUF ID through the supply chain extends the device ID traceability for tackling against malicious counterfeiting at not only chip fabrication but also package/board assembly steps. A wireless connectivity through inductive coupling of an on-chip coil is utilized for the interaction between a PUF circuit and package/board materials. No any additional fabrication or assembly processes are needed as the on-chip coil can be formed by using existing on-chip interconnect resources. A coupled chaos oscillator is employed as the PUF core for a good synergistic usage with the wireless connectivity at a high-frequency RF signaling regime. An inherent parametric sensitivity of chaos exhibits the ID uniqueness and unclonability stemmed from the CPB process/assembly static variations. This sensitivity however makes PUF to be susceptible to the dynamic disturbances in voltage and temperature. Associated instability is suppressed by an analog replica-based feedback compensation circuit with digital post-processing to guarantee the ID reproducibility. The proposed interactive PUF concept is successfully demonstrated with prototype measurement using a 0.18um CMOS test chip in a simple Chip-on-Board (CoB) assembly.