Reviewerly/exHarmony huggingface dataset:
Another bend in the orogen? Kinematics of the winding Mexican Fold-and-Trust Belt: Paleomagnetism of the Nazas arc.

The Mexican Fold-and-Trust Belt is a winding belt that formed after a series of protracted tectonic events, which began with the onset of sedimentation in the basins formed during the break-up of Pangea and during the roll-back of the oceanic Kula plate in Jurassic times. Later, the continued subduction of Kula-Farallon constituent plates at the western margin of Mexico triggered arc formation, thrusting, basin inversion, and folding. The kinematics of the fold-and-thrust belt curvature at regional and local scales is still debated. Different hypotheses have been explored to explain the trace of the orogen: 1) curvature is mainly and primarily controlled by the basin architecture; 2) Curvature is progressive and was at least partially acquired during deformation being controlled by the basement geometry, lithologic heterogeneities, and/or the shortening direction, and 3) The curvature is, at least partially, postdating the main orogenic deformation structures being formed as the youngest of the deformation phases. Several regional-scale studies have been performed (Eguiluz et al., 2010; Fitz-Diaz et al., 2017). But most of the previous paleomagnetic studies were focused on studying the configuration of the desegregated fragments of Pangea (Molina-Garza et al., 1992). In this study, we investigate the nature of the curvature in the north and central-eastern part of the Mexican Fold and Trust Belt using paleomagnetic data obtained from the Jurassic rocks of the Nazas Formation, this formation is composed of a volcanic and volcano-sedimentary succession of andesites and dacite flows interbedded with tuff deposited in an intra-arc or back-arc setting. Our results show remagnetizations, some potential primary magnetizations, and significant counterclockwise rotations. This implies a potential oroclinal bending origin for at least part of the Mexican fold-and-thrust-belt curvature. This contribution will discuss the potential mechanisms causing the curvature and the implications for the kinematics, tectonic and geodynamic evolution of the central-western Pacific subduction during the Mesozoic-Paleogene in northeastern Mexico. This work is a posthumous contribution of Dr. Roberto Stanley Molina-Garza and a tribute to his huge contribution to the understanding of the tectonic history of Mexico.Keywords: Paleomagnetism; Mexican Fold and Trust Belt; Anticlockwise rotation; Remagnetization; Jurassic; Nazas Arc.
SECURE SOFTWARE DEVELOPMENT LIFECYCLE: A CASE FOR ADOPTION IN SOFTWARE SMES

Software is widely deployed and used for managing critical daily domestic, social, and economic activities. Due to software’s economic value, software is a high-value target of malicious actors and a primary source of many information security vulnerabilities. Software must be engineered to be secure because of its value. Traditional approaches to software security treat software as an addon and have been proven inadequate at producing secure software. Practicing the secure software development lifecycle (SSDLC) is recommended in academic literature. Software SMEs must adopt and practice the SSDLC for increased security of published software. This paper explores the SSDLC and makes a case for its adoption with the goal of informing security decision-makers of Software SMEs.
Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations

Abstract Recently, convolutional neural networks (CNNs) have achieved excellent performance for the recommendation system by extracting deep features and building collaborative filtering models. However, CNNs have been verified susceptible to adversarial examples. This is because adversarial samples are subtle non-random disturbances, which indicates that machine learning models produce incorrect outputs. Therefore, we propose a novel model of Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations, named ANCF in short, to address the adversarial problem of CNN-based recommendation system. In particular, the proposed ANCF model adopts the matrix factorization to train the adversarial personalized ranking in the prediction layer. This is because matrix factorization supposes that the linear interaction of the latent factors, which are captured between the user and the item, can describe the observable feedback, thus the proposed ANCF model can learn more complicated representation of their latent factors to improve the performance of recommendation. In addition, the ANCF model utilizes the outer product instead of the inner product or concatenation to learn explicitly pairwise embedding dimensional correlations and obtain the interaction map from which CNNs can utilize its strengths to learn high-order correlations. As a result, the proposed ANCF model can improve the robustness performance by the adversarial personalized ranking, and obtain more information by encoding correlations between different embedding layers. Experimental results carried out on three public datasets demonstrate that the ANCF model outperforms other existing recommendation models.
Class-wise knowledge distillation

Knowledge distillation (KD) transfers knowledge of a teacher model to improve the performance of a student model which is usually equipped with a lower capacity. The standard KD framework, however, neglects that the DNNs exhibit a wide range of class-wise accuracy and the performance of some classes is even decreased after distillation. Observing the above phenomena, we propose a novel Class-Wise Knowledge Distillation method to find the hard classes with a simple yet effective technique and then make the students take more effort to learn these hard classes. In the experiments on image classification tasks using CIFAR-100 dataset, we demonstrate that the proposed method outperforms the other KD methods and achieves excellent performance enhancement on various networks.
Multiclass malicious URL attack type detection via capsule-based neural network

Despite the variety of cybercrimes, malicious Uniform Resource Locators (URLs) remain one of the most common threats to cybersecurity and bring huge economic losses every year. How to detect malicious URLs accurately has attracted great interests from both academia and industry. However, few focus on the multiclass malicious URL attack type detection and existing methods cannot provide robust performance due to the diversity of obfuscation strategies. In this paper, we propose a capsule-based deep neural network for malicious URL detection and classification, using character-level information from the URL string sequences. To be specific, our method transforms an input URL into character-level embedding representation firstly, then passes it into the designed convolution module to extract local features of different sizes and the local features are fed into the designed capsule module to retain the spatial hierarchical relationship of the URL string, extract accurate feature representation and output the accurate classification result finally. The experimental results on a public dataset constructed by four different classes of URLs show that compared with other baseline methods, our capsule-based method can achieve better detection and classification results, with F1-score of benign URL, malware URL, defacement URL and phishing URL at 98.94%, 95.81%, 99.63% and 94.04%, respectively. Due to the excellent performance of our capsule-based method for the detection of malicious URLs, it could be deployed in the main-stream web browsers to identify URL attack types and intercept malicious URLs effectively to protect vulnerable users against cyberattacks.
CRIME VICTIMIZATION OF NEUST CRIMINOLOGY STUDENTS LIVING IN BOARDING HOUSES AT SUMACAB ESTE, CABANATUAN CITY

Being a victim of crime is a phenomenon which may not be common to everyone, but is of great probability of happening anytime in our lives. This is because no one can actually predict and control the actions of other people especially those who intend to commit them. It is therefore important, that for us not be victimized, we have to create a certain kind of security within us, that is, we police ourselves. It is not enough that we depend on our police force for our security, but more importantly, we have to be responsible enough to keep ourselves secure and not put ourselves in a compromising situation that could lead to victimization. In the Philippines, the Philippine National Police (PNP) (2006) stated that they are committed in ensuring public safety and reducing the fear and the incidence of crime in the community. However, they acknowledge that there are many things that each and every community member can do to reduce their chances of becoming a victim or prevent the incidence of crime from happening.
The Influence of Transparent Internal Communication on Employees' Adaptation to Firm Changes in the Context of the Covid-19 Pandemic

This study investigates the impact levels of transparent internal communication on employees' adaptation to firm changes in the context of the covid-19 pandemic. This study uses data of employees of enterprises in Hanoi City, Vietnam during 2021-2022. The data used for analysis and regression consists of 618 observations. We use Cronbach’s Alpha, EFA and regression model to learn the effect of different variables on employee uncertainty about organizational change. The results show that (i) transparent internal communication has a negative effect on the control coping strategy of employees; (ii) transparent internal communication has a positive effect on the escape strategy of employees; (iii) transparent internal communication has a negative effect on the employee uncertainty about organizational change; (iv) control coping strategy has a negative effect on employee uncertainty about organizational change; and (v) Escape Strategy has a positive effect on employee uncertainty about organizational change. Based on the findings, recommendations are given for improving the enterprises maintain a good quality of relationship with their employees.
Business Policies and Practices for Ensuring Data Security: An Exploratory Study

Business policies are the formal and informal rules that govern the behaviour of organizations. The increasing dependence on technology and the rise of cybercrime have made it imperative for businesses to focus on data security policies. In this research paper, the focus is on examining the various business policies related to data security, the impact they have on the overall security of an organization and their effectiveness. Today's organizations operate and compete in a way that has undergone a fundamental digital shift. Data governance is becoming more important as a result of the increasing number of data that businesses produce, manage, and store. Furthermore, computing systems are more complicated than they used to be, frequently encompassing the public cloud, the enterprise data centre, and a wide range of edge devices, including Internet of Things (IoT) sensors, robots, and remote servers. Its intricacy increases the attack surface, making it harder to secure and monitor.The paper will be using a qualitative approach to collect data through interviews with IT professionals and managers. The objective of this research is to provide a comprehensive understanding of the current business policies related to data security and how they are being implemented. The paper will also explore the challenges faced by organizations in implementing these policies and the measures taken to overcome them. The research paper will present the findings of the study and draw conclusions based on the data collected.
Arbitrary-Shaped Text Detection with B-Spline Curve Network

Text regions in natural scenes have complex and variable shapes. Directly using contour coordinates to describe text regions will make the modeling inadequate and lead to low accuracy of text detection. To address the problem of irregular text regions in natural scenes, we propose an arbitrary-shaped text detection model based on Deformable DETR called BSNet. The model differs from the traditional method of directly predicting contour points by using B-Spline curve to make the text contour more accurate and reduces the number of predicted parameters simultaneously. The proposed model eliminates manually designed components and dramatically simplifies the design. The proposed model achieves F-measure of 86.8% and 87.6% on CTW1500 and Total-Text, demonstrating the model's effectiveness.
NEW MEDIA PRESENCE AND IMPACT OF SOCIAL MEDIA ON YOUTH

In this paper, we wish to bring to light the impacts of social media especially to the youth of this particular generation. These impacts are both negative and positive. Over the previous 2 years, social media have received so much growth as well as popularity globally to a degree a large number of scientists are nowadays keen on learning much more about these social platforms as well as the influences of theirs on the community. was via random sampling programs. The entire sample size was fifty. The outcome indicated basic info of the respondents suggested that cent % of the respondents had been belonging to 18 to 20-year age group. Social media encourages images, video clips, and unethical pictures among youth, anti-religious posting, and links produce hatred among individuals of various communities, Negative use of social networking is actually deteriorating the connection with the places, social networking is actually playing a vital role to develop awareness among youth. Keywords: Social, media, society, youth.
The use of multi-source information fusion in dam safety evaluation

With the improvement of dam safety and the diversification of monitoring methods, the demand for theoretical methods of a comprehensive evaluation of dam safety is getting higher and higher. As a newly emerging frontier and promising research field, the information fusion theory has been widely used in the process of multi-decision information processing. In this paper, the application of the multi-source information fusion method in dam safety evaluation is briefly introduced, and the theory of multi-source fusion optimization of VIKOR is proposed, which makes the final evaluation results more reasonable and reliable.
Deployment of the mobile seismic network around Petrinja in the aftermath of the Petrinja ML 6.2 earthquake

Deployment of the mobile seismic network around Petrinja in the aftermath of the Petrinja earthquakeAfter the devastating Petrinja earthquake (ML 6.2) in December 2020, the Croatian Seismological Survey acquired 20 sets (seismographs and accelerographs ) to use as a mobile pool of seismic stations. From January 2021, the installation of more than 20 stations in the wider Petrinja area began. All stations were operational in less than two weeks. Initially, the mobile pool worked offline until a working data transmission solution was found, as DynDNS didn't work with the current cellular network operator. Finally, a VPN solution was applied by installing an OpenVPN server and creating a VPN network that was proven to work properly. Since April 2021, all stations have been online and transmitting data to the CSS headquarters, where the SeisComP3 machine is responsible for data acquisition and processing. After the Ljubinje earthquake in April 2022, some of the stations of the Petrinja mobile pool were relocated to the wider Dubrovnik area. Both sites use accelerographs and seismographs for earthquake detection purposes, with 100 Hz and 200 Hz sampling for seismographs and 200 Hz for accelerographs.The Petrinja Mobile Network is still active today as the aftershock sequence is still active in that area. Details of the equipment purchased, problems encountered and solutions found for data transmission, site selection procedures applied and results achieved are presented.
Reversible data hiding in encrypted domain based on color image channel correlation

Nowadays, due to the increasing need for cloud storage and cloud computing services, digital images need to be kept confidential in order to secure the privacy of individuals' information. Since the currently available codomain reversible data masking techniques are based on gray images, this paper introduces a data masking approach using color image channel correlation to achieve codomain reversibility. In the encryption stage, the security and homomorphism of encryption are achieved through XOR encryption and block scrambling. The data hiding processed by making full use of the texture information data of the reference channel to predict the pixel data more accurately, and an adaptive prediction error expansion technique is used to embed the encrypted information into the R, G, and B channels. The experimental results prove that the method has better security and better performance than the existing algorithms.
Support samples guided adversarial generalization

Adversarial training proves to be the most effective measure to classify adversarial perturbation, which is imperceptible but can drastically alter the output of the classifier. We review various theories behind the relationship between generalization gap and adversarial robustness and then raise the question: is it the input near the decision boundary that provides guidance for the classifier to learn the ideal decision boundary and therefore yield a more desired outcome? We provide quantitative confirmation that the expected required sample size correlates favorably with sample distance and further investigate the relationship between the robust classification error and the expected distance from the decision boundary to samples. Experimental results reveal that applying the data near the decision boundary as training sets can significantly promote adversarial generalization, which keeps consistence with the main conjectures presented in this work.
OGC Testbed-18 Machine Learning Training Datasets Task: Application of standards to Machine Learning training datasets

Training datasets are a crucial component of any machine learning approach, with significant human effort spent creating and curating these for specific applications. However, a historical absence of standards has resulted in inconsistent and heterogeneous training datasets with limited discoverability and interoperability. Therefore, there is a need for best practices and guidelines for generating, structuring, describing, and curating training datasets.The Open Geospatial Consortium (OGC) Testbed-18 initiative covered several topics related to geospatial data, focussing on issues around cataloguing and interoperability. Within Testbed-18, the Machine Learning Training Datasets task aimed to develop a foundation for future standardization of training datasets for Earth observation applications.For this task, members from Pixalytics, FrontierSI, and Curtin University authored an Engineering Report that reviewed:&amp;#183; &amp;#160; &amp;#160; &amp;#160; Examples of how training datasets have been used in Earth observation applications&amp;#183; &amp;#160; &amp;#160; &amp;#160; The current best-practice methods for documenting training datasets&amp;#183; &amp;#160; &amp;#160; &amp;#160; The various requirements for training dataset metadata&amp;#183; &amp;#160; &amp;#160; &amp;#160; How the Findability, Accessibility, Interoperability, and Reuse (FAIR) principles apply to training datasetsThe Engineering Report provides a foundation that OGC can leverage in creating the future standard for machine learning training data for Earth observation applications. The Engineering Report also provides a useful overview of the state of work and key considerations for anyone wishing to improve how they document their training datasets.In our presentation, we discuss the key findings from the Engineering Report, including key metadata identified from Earth observation use cases, the current state of the art, thoughts on cataloguing and describing training data quality, and how the FAIR principles apply to training data.&amp;#160;
Portable X-Ray Fluorescence calibration – Workflow to Optimize the Analysis of Carbonates.

Portable X-Ray fluorescence (PXRF) instruments have become more and more common tools in the last few years for chemical analysis (major, minor and some traces elements) in carbonates and sedimentary rocks. PXRF is a relatively cheap and fast technique, non-destructive and prevents tedious sample preparation. Manufacturers calibrations are usually made for a very large range of rock types or materials but specific empirical calibrations are essential to improve the quantification of multi-elemental concentrations.We present a general workflow for building an empirical calibration model for a PXRF spectrometer from choosing reference materials to validating calibration&amp;#8217;s efficiency and quantifying its limit of quantification. We also compare the quantified results on a sedimentary rock suite obtained from sixteen calibrations that were built using several software available on the market, including a simple homemade spreadsheet, the Bruker calibration software named EasyCal, a free and open-source calibration app named CloudCal, and the built-in GeoMining and GeoExplorer calibrations provided by Bruker manufacturer on the Tracer 5 PXRF. This decoupled approach allows (i) to explore the pros and cons for each calibration software solutions, (ii) to investigate and weight the effect of each correction parameters (i.e. regression line geometry, peaks interferences, spectra normalization, influence coefficients, multi-phases excitation conditions) on the quantified results and (iii) to propose an original and empirical approach to estimate the lower limit of quantification (LOQ) of a PXRF instrument and its calibration.For quality check, each of these newly built calibrations was then validated on carbonate samples of known composition, corresponding to a series of Devonian limestones (Belgium) showing a large CaCO3 content ranging between 20 and 99 wt% and for which powder press pellets were formerly measured on a laboratory WD-XRF. These matrix-match empirical calibrations are systematically bringing improvement of the quantification efficiency, compared to the manufacturer calibrations.&amp;#160;
Influence of Environmental Parameters on Highly Sensitive Instruments at Moxa Geodynamic Observatory

Strainmeters measure the change in length between two known fixed points and are used primarily to identify and estimate tectonic strain. In addition to tectonic strain, these instruments also record changes in strain caused by other phenomena such as Earth tides and fluctuations of meteorological signals like changes in groundwater levels caused by precipitation, which may be much larger than tectonic strain and thus mask its signals. To avoid meteorological influences as far as possible, horizontal strainmeters often are maintained in galleries such that tectonically induced strain signals are not affected by other sources of noise.At Moxa Geodynamic Observatory, located in central Germany, two laser strainmeters with a base length of 26 m each are maintained in galleries oriented north-south and east-west. Their resolution is in the nano-scale and sampling rate is 0.1 Hz. Mountain overburden in the gallery is comparatively low at approx. 30 m and in addition, the hydrogeological situation of the subsurface surrounding the observatory, is very heterogeneous. Therefore, amplitudes of meteorological phenomena are still quite high. In order to correct meteorological influences in the recorded strain time-series, they first need to be better understood. For doing so, long time-series - a decade at least &amp;#8211; are needed. As the laser strainmeters continuously record since summer 2011, these are now available.We present the results of weighting various meteorological parameters on the strainmeter recordings by training Long Short Term Memory Networks and perturbing input parameters for the test data. In this way, the contribution of each parameter to the meteorologically induced strain signals can be estimated. This knowledge is subsequently used to eliminate meteorological influences from the time-series recordings of strain.