Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyberattack and Machine-Induced Fault Detection and Isolation Methodologies for Cyber-Physical Systems

In this article, the problem of simultaneous cyberattack and fault detection and isolation (CAFDI) for both centralized and large-scale interconnected cyber-physical systems (CPSs) is studied. The proposed methodologies include centralized and distributed CAFDI approaches, which involve the use of two filters on the plant and command and control (C&C) sides of the CPS, as well as an unknown input observer (UIO)-based detector on the plant side. The article characterizes the conditions under which the proposed methodologies can detect various types of deception attacks, such as covert attacks, zero dynamics attacks, and replay attacks. In the proposed centralized CAFDI methodology, the transmission of estimates from the C&C side filter to the plant side is required, with the assumption that a certain number of communication channels are secured. Consequently, a bank UIO-based detectors are utilized on the plant side to detect and isolate anomalies. It is also assumed that adversaries have knowledge of system parameters, filters, and the UIO-based detector. To address the limitations of secure communication channels, modifications to the two side filters and the UIO-based detector have been developed and implemented that eliminates the need for any secured communication channel in the modified CAFDI module. However, information must now be sent to and received from the plant side filter. Consequently, we develop a distributed CAFDI methodology for the interconnected large-scale CPS which consists of several subsystems. Finally, a hardware-in-the-loop (HIL) simulation of a four-area power network system under presence of both cyberattacks and faults using an OPAL-RT real-time simulator and Raspberry Pi is provided to illustrate the effectiveness of our proposed distributed CAFDI methodology.
The Shooting Regressor; Randomized Gradient-Based Ensembles

An ensemble method is introduced that utilizes randomization and loss function gradients to compute a prediction. Multiple weakly-correlated estimators approximate the gradient at randomly sampled points on the error surface and are aggregated into a final solution. A scaling parameter is described that controls a trade-off between ensemble correlation and precision. Numerical methods for estimating optimal values of the parameter are described. Empirical results are computed over a popular dataset. Inferential statistics on these results show that the method is capable of outperforming existing techniques in terms of increased accuracy.
Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction

This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk measure. The sampling-based model predictive control relies on mode insertion gradient optimization for this risk measure as well as Trajectron++, a state-of-the-art generative model that produces multimodal probabilistic trajectory forecasts for multiple interacting agents. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control, which is advantageous compared to end-to-end policy learning methods in that it allows the robot’s desired behavior to be specified at run time. In particular, we show that the robot exhibits diverse interaction behavior by varying the risk sensitivity parameter. A simulation study and a real-world experiment show that the proposed online framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
Fraud Detection using Data-Driven approach

The extensive use of the internet is continuously drifting businesses to incorporate their services in the online environment. One of the first spectrums to embrace this evolution was the banking sector. In fact, the first known online banking service came in 1980. It was deployed from a community bank located in Knoxville, called the United American Bank. Since then, internet banking has been offering ease and efficiency to costumers in completing their daily banking tasks. The ever increasing use of internet banking and a large number of online transactions increased fraudulent behavior also. As if fraud increase was not enough, the massive number of online transactions further increased the data complexity. Modern data sources are not only complex but generated at high speed and in real-time as well. This presents a serious problem and a definite reason why more advanced solutions are desired to protect financial service companies and credit cardholders. Therefore, this research paper aims to construct an efficient fraud detection model which is adaptive to customer behavior changes and tends to decrease fraud manipulation, by detecting and filtering fraud in real-time. In order to achieve this aim, a review of various methods is conducted, adding above a personal experience working in the Banking sector, specifically in the Fraud Detection office. Unlike the majority of reviewed methods, the proposed model in this research paper is able to detect fraud in the moment of occurrence using an incremental classifier. The evaluation of synthetic data, based on fraud scenarios selected in collaboration with domain experts that replicate typical, real-world attacks, shows that this approach correctly ranks complex frauds. In particular, our proposal detects fraudulent behavior and anomalies with up to 97\% detection rate while maintaining a satisfyingly low cost.
DualLip: A System for Joint Lip Reading and Generation

Lip reading aims to recognize text from talking lip, while lip generation aims to synthesize talking lip according to text, which is a key component in talking face generation and is a dual task of lip reading. Both tasks require a large amount of paired lip video and text training data, and perform poorly in low-resource scenarios with limited paired training data. In this paper, we develop DualLip, a system that jointly improves lip reading and generation by leveraging the task duality and using unlabeled text and lip video data. The key ideas of the DualLip include: 1) Generate lip video from unlabeled text using a lip generation model, and use the pseudo data pairs to improve lip reading; 2) Generate text from unlabeled lip video using a lip reading model, and use the pseudo data pairs to improve lip generation. To leverage the benefit of DualLip on lip generation, we further extend DualLip to talking face generation with two additionally introduced components: lip to face generation and text to speech generation, which share the same duration for synchronization. Experiments on GRID and TCD-TIMIT datasets demonstrate the effectiveness of DualLip on improving lip reading, lip generation and talking face generation by utilizing unlabeled data, especially in low-resource scenarios. Specifically, on the GRID dataset, the lip generation model in our DualLip system trained with only 10% paired data and 90% unpaired data surpasses the performance of that trained with the whole paired data, and our lip reading model achieves 1.16% character error rate and 2.71% word error rate, outperforming the state-of-the-art models using the same amount of paired data.
Accurate and Lightweight Image Super-Resolution With Model-Guided Deep Unfolding Network

Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed like black boxes lacking transparency and interpretability. Moreover, the improvement in visual quality is often at the price of increased model complexity due to black-box design. In this paper, we present and advocate an explainable approach toward SISR named model-guided deep unfolding network (MoG-DUN). Targeting at breaking the coherence barrier, we opt to work with a well-established image prior named nonlocal auto-regressive model and use it to guide our DNN design. By integrating deep denoising and nonlocal regularization as trainable modules within a deep learning framework, we can unfold the iterative process of model-based SISR into a multistage concatenation of building blocks with three interconnected modules (denoising, nonlocal-AR, and reconstruction). The design of all three modules leverages the latest advances including dense/skip connections as well as fast nonlocal implementation. In addition to explainability, MoG-DUN is accurate (producing fewer aliasing artifacts), computationally efficient (with reduced model parameters), and versatile (capable of handling multiple degradations). The superiority of the proposed MoG-DUN method to existing state-of-the-art image SR methods including RCAN, SRMDNF, and SRFBN is substantiated by extensive experiments on several popular datasets and various degradation scenarios.
A Detail Study of Security and Privacy issues of Internet of Things

The Internet of Things, or IoT, refers to the billions of physical objects around the planet that are now connected to the Internet, many of which store and exchange the data without human interaction. In recent years the Internet of Things (IoT) has incredibly become a groundbreaking technical innovation that has contributed to massive impact in the ways where all the information is handled incorporate companies, computer devices, and even kitchen equipment and appliances, are designed and made. The main focus of this chapter is to systematically review the security and privacy of the Internet of Things in the present world. Most internet users are genuine, yet others are cybercriminals with individual expectations of misusing information. With such possibilities, users should know the potential security and privacy issues of IoT devices. IoT innovations are applied on numerous levels in a system that we use daily in our day-to-day life. Data confidentiality is a significant issue. The interconnection of various networks makes it impossible for users to assert extensive control of their data. Finally, this chapter discusses the IoT Security concerns in the literature and providing a critical review of the current approach and proposed solutions on present issues on the Privacy protection of IoT devices.
Unsupervised Domain Adaptation by Uncertain Feature Alignment

Unsupervised domain adaptation (UDA) deals with the adaptation of models from a given source domain with labeled data to an unlabeled target domain. In this paper, we utilize the inherent prediction uncertainty of a model to accomplish the domain adaptation task. The uncertainty is measured by Monte-Carlo dropout and used for our proposed Uncertainty-based Filtering and Feature Alignment (UFAL) that combines an Uncertain Feature Loss (UFL) function and an Uncertainty-Based Filtering (UBF) approach for alignment of features in Euclidean space. Our method surpasses recently proposed architectures and achieves state-of-the-art results on multiple challenging datasets. Code is available on the project website.
Analysis of Polymorphism on KAP6.2 and KAP7 Gene in Three Goat Breeds with PCR-SSCP

The polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP)was used to detected the polymorphism of keratin associated protein (KAP6.2, KAP7) in three goat breeds. The results show that the KAP7 didn't present polymorphism in all three breeds while the KAP6.2 shows three Genotypes: AA, AB and BB. The frequency of all pattern types analyzed by PCR-SSCP were Hardy-Weinberg equilibrium in Liaoning goat breed, however the same result didn't appear in Liaoning-Kelan crossed goat breed and LiCheng goat breed. The different genotype homozygote on KAP6.2 were cloned and sequenced. The results indicated that both AA and BB have nucleotide substitution , the AA genotype presents the following mutation: 107 bp(A-G),196 bp(C-T),199 bp(T-G),252 bp(A-T),265 bp(C-T),291 bp(A-G),292 bp(T-C),345 bp(T-G),and the BB genotype: 164 bp(T-C),194 bp(T-C),199 bp(T-G),201 bp(C-G),233 bp(A-G),275 bp(A-G),299 bp(A-G),309 bp(G-C),327 bp(A-G),352 bp(G-T)。 The sequence of amino acids was analyzed, the result showed that some of the mutations in AA and BB genotype caused amino acid change.
Phantom Epistasis in Genomic Selection: On the Predictive Ability of Epistatic Models

Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density (“Phantom Epistasis”). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.
Here and now: Lived experiences of professional nurses practising caring presence in a rural public hospital in the North West Province, South Africa

Background Practising caring presence is recognised as an important nursing intervention indispensable to high-quality, patient-centred care. An awareness of the real world of professional nurses (PNs) practising caring presence will assist in expanding and supporting the existing literature on the same. A clear and rich description of what the concept of caring presence entails within the unique South African nursing context may guide nurses in the art of this nursing skill, enhance their professionalism and facilitate the formulation of recommendations on how to encourage nurses to implement the practice of caring presence within nursing. Aim This study explored and described the lived experiences of PNs practising caring presence in a rural public hospital. Setting The study setting was a 120-bed, level-two district hospital in the North West Province of South Africa. Methods A descriptive phenomenological method, specifically Husserl’s approach, informed this study. Semi-structured interviews were conducted with a purposive sample of 10 PNs. Data were coded and analysed using Colaizzi’s seven-step method. Results Five themes emerged from the data analysis: professional caring presence, ethical caring presence, personal caring presence, healing caring presence and what caring presence is not. Conclusion Professional nurses experience practising caring presence as professionally and personally fulfilling, as an expression of their passion for the profession, as a way of portraying ethical care, as a willingness to be personally present and as a healing experience that involves commitment and taking care of patients holistically. Unprofessional, unethical behaviour and depersonalisation of patients were indicated as uncaring behaviour.
Isolation,Purity and Identification of Hepatocytes in Rat Normal Liver and Regenerating Liver

Rat 2/3 hepatectomy model was made by the method of Higgins et al.,hepatic cells were scattered by two-step perfusion,and hepatocytes were isolated and pruified by density gradient centrifugation with 60 % percoll. Immunocytochemistry method was used to qualitify and localize albumin(ALB) and glucose-6-phosphatase(G-6-P) in liver tissue,the dispersed hepatic cells,and the purified hepatoctyes. The exrpressions of alb and G-6-P were quantified using RT-PCR. The results primarily showed that ALB/G-6-P postive hepatocytes account up more than 96 % of the total hepatocytes;mRNA levels of ALB and G-6-P in the purified hepatocytes were stable in the Purificl hepatocytes of rat regenerating liver,and also was the content of the corresponding proteins,indicating the modified method for hepatocytes purification in this study has the adventage of high hepatocyte harvest,high purity and survival rate.
Platelet δ-Storage Pool Disease: An Update

Platelet dense-granules are small organelles specific to the platelet lineage that contain small molecules (calcium, adenyl nucleotides, serotonin) and are essential for the activation of blood platelets prior to their aggregation in the event of a vascular injury. Delta-storage pool diseases (δ-SPDs) are platelet pathologies leading to hemorrhagic syndromes of variable severity and related to a qualitative (content) or quantitative (numerical) deficiency in dense-granules. These pathologies appear in a syndromic or non-syndromic form. The syndromic forms (Chediak–Higashi disease, Hermansky–Pudlak syndromes), whose causative genes are known, associate immune deficiencies and/or oculocutaneous albinism with a platelet function disorder (PFD). The non-syndromic forms correspond to an isolated PFD, but the genes responsible for the pathology are not yet known. The diagnosis of these pathologies is complex and poorly standardized. It is based on orientation tests performed by light transmission aggregometry or flow cytometry, which are supplemented by complementary tests based on the quantification of platelet dense-granules by electron microscopy using the whole platelet mount technique and the direct determination of granule contents (ADP/ATP and serotonin). The objective of this review is to present the state of our knowledge concerning platelet dense-granules and the tools available for the diagnosis of different forms of δ-SPD.
Combined effect of recombinant human adenovirus p53 and curcumin in the treatment of liver cancer

The development of an effective therapeutic intervention for liver cancer is a worldwide challenge that remains to be adequately addressed. Of note, TP53, which encodes the p53 protein, is an important tumor suppressor gene, 61% of TP53 is functionally inactivated in liver cancer. Recombinant human adenovirus p53 (rAd-p53) is the first commercial product that has been used for gene therapy. In the present study, the combined mechanistic effects of rAd-p53 and curcumin, a naturally occurring compound with previously reported anti-inflammatory, antioxidant and anti-cancer properties, were assessed in liver cancer cells, using HepG2 cells as the model cell line. The administration of either curcumin or rAd-p53 promoted apoptosis, suppressed epithelial-mesenchymal transition (EMT) and blocked G2/M phase progression in HepG2 cells, which were potentiated further when both agents were applied together. Combined rAd-p53 and curcumin treatment resulted in higher p53 (P<0.01) and p21 (P<0.01) expression compared with rAd-p53 or curcumin were added alone, suggesting an additive effect on TP53 expression. Additionally, curcumin and rAd-p53 were demonstrated to regulate the activation of mitogen-activated protein kinases (MAPKs) ERK1/2, p38 MAPK and JNK. These results indicated that the combination of rAd-p53 with curcumin synergistically potentiates apoptosis and inhibit EMT compared with either rAd-p53 or curcumin treatment alone via the regulation of TP53 regulation. Mechanistically, this effect on TP53 expression may involve the ERK1/2, p38 MAPK and JNK signaling pathways. The current study provides new insights that can potentially advance the development of therapeutic strategies for liver cancer treatment.
Comprehensive Analysis of Zinc Derivatives Pro-proliferative, Anti-Apoptotic and Antimicrobial Effect on Human Fibroblasts and Keratinocytes in a Simulated, Nutrient-deficient Environment In Vitro

Zinc as therapeutic agent in skin and wound care has been known for centuries, but its role is controversial and comprehensive investigations in nutrient-deficient environments are lacking. We aimed to provide a broad analysis of different zinc derivatives on proliferation, apoptosis and antimicrobial properties in a simulated nutrient-deficient environment in vitro. Human fibroblasts (CRL2522) and keratinocytes (HaCaT) were treated with a broad concentration range (10 – 0.0001 µg/mL) of zinc-sulfate (ZnSO4), -gluconate (ZnGluc) and -histidine (ZnHis) for 1-6 days under nutrient-deficient media conditions. Cell proliferation was investigated by XTT assay. Targeted analyzes in proliferation (E2F1, PCNA) and apoptosis (TP53) associated genes were performed via qRT-PCR and apoptosis was determined via FACS (annexin V/7-AAD staining). Antimicrobial efficacy was investigated using a quantitative suspension method against S. aureus, P. aeruginosa, E. coli, and C. albicans. The results indicated that 0.1 to 0.001 µg/mL Zn increased cell proliferation in both cell lines. Fibroblasts were more susceptible with significant proliferation peaks on days 2 & 6, and days 1 & 4 for keratinocytes. No relevant changes in gene expression were detected for E2F1 and PCNA nor for TP53. Annexin-V/7-AAD-staining of fibroblasts revealed a small, yet insignificant reduction of apoptosis induction for ZnGluc and ZnSO4. ZnGluc and ZnSO4 (0.1%) achieved high microbial reductions (4-5 log10 reductions) against tested pathogens. ZnGluc and ZnSO4 showed relevant pro-proliferative and antimicrobial, as well as tendential anti-apoptotic features in a simulated nutrient-deficient microenvironment in vitro. This further validates a potential benefit of local zinc treatment in deficient wound microenvironments.
ZBED1/DREF: A transcription factor that regulates cell proliferation

Maintenance of genomic diversity is critically dependent on gene regulation at the transcriptional level. This occurs via the interaction of regulatory DNA sequence motifs with DNA-binding transcription factors. The zinc finger, BED-type (ZBED) gene family contains major DNA-binding motifs present in human transcriptional factors. It encodes proteins that present markedly diverse regulatory functions. ZBED1 has similar structural and functional properties to its Drosophila homolog DNA replication-related element-binding factor (DREF) and plays a critical role in the regulation of transcription. ZBED1 regulates the expression of several genes associated with cell proliferation, including cell cycle regulation, chromatin remodeling and protein metabolism, and some genes associated with apoptosis and differentiation. In the present review, the origin, structure and functional role of ZBED1 were comprehensively assessed. In addition, the similarities and differences between ZBED1 and its Drosophila homolog DREF were highlighted, and future research directions, particularly in the area of clinical cancer, were discussed.