Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Genomic Analysis of the Lettuce Bacterial Leaf Spot Pathogen, Xanthomonas hortorum pv. vitians, to Investigate Race Specificity

Bacterial leaf spot (BLS) of lettuce caused by Xanthomonas hortorum pv. vitians (Xhv) was first described over 100 years ago and remains a significant threat to lettuce cultivation today. This study investigated the genetic relatedness of the Xhv strains and the possible genetic sources of this race-specific pathogenicity. Whole genome sequences of eighteen Xhv strains representing the three races, along with eight related Xanthomonas strains, were included in the analysis. A maximum likelihood phylogeny based on concatenated whole genome SNPs confirmed previous results describing two major lineages of Xhv strains. Gene clusters encoding secretion systems, secondary metabolites, and bacteriocins were assessed to identify putative virulence factors that distinguish the Xhv races. Genome sequences were mined for effector genes, which have been shown to be involved in race specificity in other systems. Two effectors identified in this study, xopAQ and the novel variant xopAF2, were revealed as possible mediators of a gene-for-gene interaction between Xhv race 1 and 3 strains and wild lettuce Lactuca serriola ARM-09-161-10-1. Transposase sequence identified downstream of xopAF2 and prophage sequence found nearby within Xhv race 1 and 3 insertion sequences suggest that this gene may have been acquired through phage-mediated gene transfer. No other factors were identified from these analyses that distinguish the Xhv races.
Privacy-Preserving and Customization-Supported Data Aggregation in Mobile Crowdsensing

Data aggregation is a fundamental problem in mobile crowdsensing (MCS). However, the existing approaches are still unsatisfactory considering the privacy protection of sensing data and aggregation results. In addition, most existing privacy-preserving data aggregation schemes can only support a single type of aggregation, which limits their application scenarios. To address these issues, we propose a novel privacy-preserving and customization-supported data aggregation scheme that can achieve multiple types of aggregation. Specifically, we utilize additive secret sharing (<inline-formula> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula>) to protect the privacy of both sensing data and aggregation results and then propose a simplified secure triplet generation protocol based on <inline-formula> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula> to construct secure aggregation operations. Moreover, we design a secure comparison (SC) algorithm and a secure top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> algorithm to realize customized aggregation (i.e., statistical aggregation over top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> largest or smallest values of sensing data). The formal theoretical analysis demonstrates that the proposed scheme is effective, and the extensive experiments conducted on a real-world data set show that the proposed approach is privacy preserving and efficient.
A deep learning framework for adaptive compressive sensing of high‐speed train vibration responses

Onboard monitoring plays an important role in real‐time condition assessment of rail systems. However, the data amount is typically tremendous due to the high sampling rate needed and long traveling distance, especially for vibration data collected from high‐speed trains (HSTs). As for fault diagnosis of mechanical systems, compressive sensing (CS) has been increasingly adopted to reduce the data amount. In comparison to rotary bearings and bolted joints in machinery that operate in relatively steady working environments, HSTs run in an open and varying environment throughout the traveling mileage, and the data amount is normally much larger, making it tricky to directly apply the classical CS methods. This study aims to bridge the gap by investigating the sparsity of HST vibration signals and CS approaches. Considering the lack of sparsity and long reconstruction time, we propose an efficient adaptive CS approach for dynamic responses of HSTs. More specifically, we unroll the iterative soft thresholding algorithm (ISTA) in a deep learning (DL) framework and configure it into a data reconstruction machine. Compared to the conventional CS methods, our approach exhibits two advantages: (i) The dictionary learning and signal reconstruction are integrated into one neural network and can be conducted in an end‐to‐end manner; (ii) the process is highly efficient since encapsulating ISTA in a DL framework can naturally leverage the capability of GPU. The proposed approach is validated using data collected from an in‐service HST, and results show that our approach achieves superior reconstruction performance over fixed bases and redundant dictionaries.
Teaching with Acceptance and Commitment: Building Teachers’ Social-Emotional Competencies for Teaching Effectiveness

Abstract Teachers need to relate to students in caring and committed ways, which can be personally and emotionally challenging. Enhancing teachers’ social-emotional competences (SECs) is a pathway to fostering these kinds of relationships. One possibility is using Teaching with Acceptance and Commitment (TAC), which integrates ideas and practices from mindfulness and behavior change. TAC helps teachers notice their emotional responses in the classroom and respond thoughtfully guided by professional values.
PIK3CA Mutation as Potential Poor Prognostic Marker in Asian Female Breast Cancer Patients Who Received Adjuvant Chemotherapy

Background: The prognostic relevance of the PIK3CA mutation together with PD-L1, c-Met, and mismatch repair deficiency (dMMR) have not been fully investigated in Asian women with breast cancer (BC) who have undergone postoperative adjuvant chemotherapy. Methods: We analyzed PIK3CA mutations via peptide nucleic acid (PNA)-mediated real-time PCR assay, PD-L1/c-Met expression via immunohistochemistry (IHC), and microsatellite instability (MSI) status using PCR and IHC, in 191 resected BCs from 2008 to 2011. The Cancer Genome Atlas (TCGA) dataset for the involvement of the PIK3CA mutation with PD-L1/c-Met/MMR was explored. Results: The PNA clamp-mediated assay was able to detect the PIK3CA mutation in 1% of the mutant population in the cell line validation. Using this method, the PIK3CA mutation was found in 78 (49.4%) of 158 samples. c-Met and PD-L1 positivity were identified in 31.4 and 21.8% of samples, respectively, which commonly correlated with high histologic grade and triple-negative subtype. MSI/dMMR was observed in 8.4% of patients, with inconsistency between MMR IHC and the MSI PCR. The PIK3CA mutation exhibited a poor prognostic association regarding recurrence-free survival (RFS) in both overall and triple-negative BCs. In subgroup analyses, the PIK3CA-mutated tumors showed poorer RFS than the PIK3CA-wildtype within the c-Met-positive, MSS, triple-negative, or age onset <50 years subgroups, which showed a similar trend of association in TCGA data. Conclusions: PIK3CA mutation together with c-Met or dMMR/MSI status might be relevant to poor prognosis in BC subsets, especially in Asian women.
IIoT Deep Malware Threat Hunting: From Adversarial Example Detection to Adversarial Scenario Detection

Protecting widely used deep classifiers against black-box adversarial attacks is a recent research challenge in many security-related areas, including malware classification. This class of attacks relies on optimizing a sequence of highly similar queries to bypass given classifiers. In this article, we leverage this property and propose a history-based method named, stateful query analysis (SQA), which analyzes sequences of queries received by a malware classifier to detect black-box adversarial attacks on an industrial Internet of Things (IIoT). In the SQA pipeline, there are two components, namely the similarity encoder and the classifier, both based on convolutional neural networks. Unlike the state-of-the-art methods, which aim to identify individual adversarial examples, tracking the history of queries allows our method to identify adversarial scenarios and abort attacks before their completion. We optimize SQA using different combinations of hyperparameters on an advanced risc machine (ARM)-based IIoT malware dataset, widely adopted for malware threat hunting in industry 4.0. The use of a novel distance metric in calculating the loss function of the similarity encoder results in more disentangled representations and improves the performance of our method. Our evaluations demonstrate the validity of SQA via a detection rate of 93.1% over a wide range of adversarial examples.
THE STRUCTURE OF THE BUILDING LIFE CYCLE ASSESSMENT (LCA) FOR SELECTING SUSTAINABLE MATERIALS IN RESIDENTIAL COMPLEXES IN TEHRAN

The world is now faced with an imbalance in the biological systems. The emergence of the phenomenon as climate change in the world can seriously take at risk the biological future of the world and causes the extinction of life and organisms on Earth. Given that the main reason for this phenomenon is the greenhouse gas emissions, buildings cause about half of these emissions, as a sustainable building design will cause to reduce negative effects caused by the improper use of materials and produce wastes. Therefore, sustainability approach based on the needs of today's generation using renewables resources and in opposition to non-renewable resources can be expressed. In fact, sustainable building materials should have the lowest risk to human health. So materials should be investigated from design to implementation and operation to understand the sustainability concepts of building. The present article will review the assessment of sustainable materials to use in residential buildings in the area of Tehran, and the vision of research is to provide solutions for assessing the sustainable architecture through understanding the new way of building life cycle analysis (LCA) of materials in building as a tool to measure. So the research questions are: How much selecting sustainable materials based on LCA in the design of residential complexes in Tehran is effective in reducing energy consumption of these buildings? And it investigates how quantitative and qualitative sustainable materials based on assessment system of environmental cycle in residential complex in Tehran.
EU Trade Agreements: To Mix or Not to Mix, That Is the Question

The European Union (EU) can only act internationally on competences that have been transferred to it by its Member States. Trade agreements negotiated by the EU that include provisions outside its exclusive competences should be concluded as ‘mixed’. Mixed trade agreements must be ratified following not only the procedures set out in the EU treaties, but also the national ratification procedures of the Member States. As a result, national or even regional parliaments may block trade deals agreed between the EU and its trading partners after years of negotiations. Should the EU then avoid negotiating mixed trade agreements? We argue that the answer to this question depends crucially on the objectives of the EU when negotiating with its trading partners. If the EU is mostly driven by market-access motives, it should restrict the agreement to policy areas under its exclusive competence, thus insulating the trade deal from the legal and political risks of mixity. When instead its motives are mostly political, mixity is a ‘necessary evil’ to achieve non-trade objectives. Trade Agreements, European Unity, Competences, Ratification Procedures
Evaluating an online well-being program for college students during the COVID-19 pandemic

Introduction: The global COVID-19 pandemic has aggravated challenges involving college students’ mental health and well-being. Some literature suggested developing online programs to address the pandemic’s impact on college students’ mental health and well-being. Thus, this study assessed if significant improvement in well-being among college students can be observed after introducing an online well-being program.Methods: The study utilized a quantitative methodology, mainly using a two-group pretest-posttest design on 178 college students in a private college and state university. The experimental group received 3 months of the well-being program while the control resumed their activities of daily living (ADL). The modified positive emotion, engagement, relationship, meaning, and accomplishment (PERMA) profiler questionnaire was the primary evaluation instrument that measured the participants’ well-being. The first phase gathered the participants’ relevant profile and background, and the last phase concluded with the evaluation of the program. Data were analyzed using SPSS v.21. Results: Based on the post-evaluation PERMA scores, the experimental participants (M = 7.21, SD 1.70) did not differ much from the control (M = 7.07, SD = 1.55) according to a t-test t(176) = –1.07, p = 0.57 as computed using a two-sample independent t-test at a significance level of α = 0.05. The overall PERMA score description is normal functioning. The Pearson correlation of the experimental group’s pre-test and post-test scores (r(91) = 0.01, p = 0.904) and the control (r(83) = 0.04, p = 0.732) group did not indicate an evidence of a significant relationship.Conclusion: The results do not provide evidence of a significant difference and relationship between the experimental participants’ pre-test and post-test PERMA scores after the online well-being program.
FULLY AUTOMATED 3D-CRACK PROPAGATION ANALYSES BASED ON TETRAHEDRAL FINITE ELEMENTS

This paper describes the development of software to perform three-dimensional crack propagation analyses. The software is based on the conventional finite element method with second order tetrahedral element and an automatic mesh generation software. Although hexahedral finite elements have historically been used in fracture analyses and methodologies to compute the crack parameters have been developed for the hexahedral elements, the authors successfully developed a VCCM (virtual crack closure-integral method) for the second order tetrahedral finite element in present research [1,2,3,4]. VCCM accurately computes the stress intensity factors based on an energetic approach. Use of the tetrahedral element allows us to adopt an automatic mesh generation program. Kawai et al [5] is developing a mesh generator for 3D crack analyses. The direction and rate of crack propagation are predicted based on the stress intensity factors and the shape of crack is updated. Hence, a software package containing the modules for mesh generation, for finite element analysis, for stress intensity factor evaluation, for predicting the rate and the direction of crack propagation and for updating crack configuration, is being developed. The outline of the software is illustrated in Fig. 1. At WCCM8, some example problems will be presented. Furthermore, VCCM for tetrahedral finite element has been extended for four-nodegeneralized tetrahedral finite elements with rotational and strain like degrees of freedoms at the corner nodes [6]. Some numerical results will be presented.
Visual Cryptography in Biometrics Passport

Every human being is unique in their nature such as traits and physical symptoms so computer science is using the biometric for perfect identification within large database. Visual cryptography scheme is a cryptographic technique, which allows visual information e.g. printed text, handwritten notes, and picture to be encrypted in such a way that the decryption can be performed by the human visual system, without the aid of computers. Biometric passport is a smart card technology product created by use of biometric data and computer chip for authenticate identification of citizen of particular country. Current passport has certain shortcoming. In this method it is proposed to convert scan images of retina, fingerprint and face in secret image and meaningful shares by use of visual cryptography. In this method with use of Visual Cryptography three biometrics i.e. retina image, fingerprint image and face image encrypted in two meaningful shares when these two share stacking on one another fingerprint image revealed and that can be verified with on the spot live fingerprint image for perfect identification accuracy. Government certifies the identity of citizen in Passport. As the anti social activity is become burning problem for the world in this millennium. Some governments have started to switch over on e-Passport and certain other countries including India are in process to issue e-Passport in near future. In e- Passport smart card technology with biometrics and digital signature is used. In this e-Passport human biometrics such as face image, fingerprint and Iris image utilize for identity so it is recognize as a biometric passport. The unique information of the person is store in a computer chip that is kept in passport with help of reader and the chip particular person can be identified in the safe manner. Current e-Passport shortcomings are Data may be migrated with advance technology, Obtain personal information by unethical hacking, Cloning and Cracking, Expensive for passport holder, Costly
SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources

SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in terms of performance. In addition, most current intrusion detection strategies involve collection of traffic coming into the web application either from a network device or from the web application host, while other strategies collect data from the database server logs. In this project, we are collecting traffic from two points: the web application host, and a Datiphy appliance node located between the webapp host and the associated MySQL database server. In our analysis of these two datasets, and another dataset that is correlated between the two, we have been able to demonstrate that accuracy obtained with the correlated dataset using algorithms such as rule-based and decision tree are nearly the same as those with a neural network algorithm, but with greatly improved performance.
The Influences of Trust in Leader on the Employees’ Voice Behavior: The Mediating Role of Psychological Safety

This research is based on the relevant research literature on the voice behavior, this study examines the influence of trust in leader on voice behavior. And this study examines the role of psychological safety as a mediating between trust in leader and voice behavior. This study uses SPSS for data analysis. The results of the study are as follows: This study explains the impact of trust in leader on psychological safety, and explains the impact of trust in leader on organization members' voice behavior, as well as the impact of psychological safety on organization members' voice behavior. And this study also explains the influence of psychological safety as an intermediary effect on trust in leader and voice behavior. Overall, the higher the level of organization members’ trust in leader, the more they will increase the level of psychological safety of the organization members, will prompt the organization members to make more voice behavior.
Automatic Anthropometric System Development Using Machine Learning

The contactless automatic anthropometric system is proposed for the reconstruction of the 3D-model of the human body using the conventional smartphone. Our approach involves three main steps. The first step is the extraction of 12 anthropological features. Then we determine the most important features. Finally, we employ these features to build the 3D model of the human body and classify them according to gender and the commonly used sizes.
A DEVELOPMENT OF INTELLIGENT TRAFFIC DISPATCHING AND PARKING SYSTEM WITH LIMITED PARKING LOTS: A CASE OF LOCAL CITY FESTIVAL APPLICATION

We developed an intelligent car dispatching and optimal available parking lot search system for the local festival with parking space constraints. We adopted LoRa (Long Range) network technology and Q-learning method to solve the parking bottleneck problem of the city wide distributed parking lots in the period of local festival. To develop the proposed system, we analyzed system requirements and designed the system architecture and general data processing ﬂow. We assumed that most travelers have mobile phone. They use an App which can communicate with LoRa sensor network management server. We used Poisson distribution in the queueing theory to predict inbound traﬃcs and to measure parking service performance. Q-learning algorithm is also used to ﬁnd fastest routes and dispatch the vehicles eﬃciently to the available parking lots. The simulation results were good enough to verify the eﬃciency of the proposed method.
Binary Multi Channel Morphological Neural Network

Neural networks and particularly Deep learning have been comparatively little studied from the theoretical point of view. Conversely, Mathematical Morphology is a discipline with solid theoretical foundations. We combine these domains to propose a new type of neural architecture that is theoretically more explainable. We introduce a Binary Morphological Neural Network (BiMoNN) built upon the convolutional neural network. We design it for learning morphological networks with binary inputs and outputs. We demonstrate an equivalence between BiMoNNs and morphological operators that we can use to binarize entire networks. These can learn classical morphological operators and show promising results on a medical imaging application.
Strategy Analysis for Increasing Business Performance through Innovation Capability in the Textile Industry

: This research is to find out strategies for improving business performance through the ability of innovation in the textile industry, the authors conduct a study on the effect of co-creation, entrepreneur capability, on the performance firm through innovation capability. The sample in this study is a company engaged in the textile industry in Banten province as many as 125 respondents with purposive sampling method. The data analysis technique used in this study is quantitative analysis using the SEM (Structural Equation Modeling) model of the AMOS 23.00 program. the results of the study explain that the positive influence of entrepreneur capability, on the performance firm through innovation capability, but for co-creation, the results have no effect on company performance. While the entrepreneur capability variable has the most dominant influence on company performance. The results of this study explain that the total influence of entrepreneur capability and co-creation on innovation capability has a very large impact on improving the performance of companies in the textile industry in Banten province. Directly, co-creation is not significant influence business performance. It indicates that the company should respond to it by doing the innovations.