Reviewerly/exHarmony huggingface dataset:
Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery

Authors:
raw_author_name: Sravani N
raw_author_name: Anil Kumar B

Abstract: Cross-site request forgery (CSRF) vulnerabilities pose a significant threat to web application security, enabling attackers to execute unauthorized actions on behalf of authenticated users. Conventional CSRF detection methods, such as manual code review and static analysis, are often time-consuming, error-prone, and inefficient. Proposes Mitch, a novel machine learning (ML)-based solution for the black-box detection of CSRF vulnerabilities. Mitch employs supervised learning, trained on a comprehensive dataset of HTTP requests and responses, to effectively identify security-sensitive HTTP requests and uncover CSRF vulnerabilities within them. Rigorous evaluations on a diverse set of real-world web applications demonstrate Mitch's remarkable ability to detect CSRF vulnerabilities with high accuracy, outperforming traditional methods. Mitch's automated nature eliminates the need for manual code review and static analysis, saving time and effort while reducing the risk of human error. Additionally, Mitch's scalability allows seamless integration into continuous integration and continuous delivery (CI/CD) pipelines, enabling continuous security monitoring and vulnerability detection. Mitch's efficacy extends beyond detecting known CSRF vulnerabilities. Its ability to identify patterns and relationships enables it to uncover obscure CSRF vulnerabilities that may have been overlooked by traditional methods, including zero-day vulnerabilities. In conclusion, Mitch emerges as a powerful tool for enhancing web application security, offering a comprehensive and automated solution for detecting CSRF vulnerabilities. Its ability to handle complex web applications, uncover hidden CSRF vulnerabilities, and integrate into CI/CD pipelines makes it an indispensable tool for web security professionals. Mitch's adoption has the potential to significantly reduce the risk of CSRF attacks and safeguard sensitive user data. We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. In this project, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyses, due to their diversity and the widespread adoption of custom programming practices. ML is thus very helpful for web application security it can take advantage of manually labeled data to bring the human understanding of the web application semantics into automated analysis toolsMitch allowed us to identify 35 new CSRFs on 20 major websites and 3 new CSRFs on production software. Keywords: Mitch, CSRF, CI/CD pipelines, Security Token Service (STS), Same-Origin Policy (SOP).
On Time-based Exploration of LMS Data and Prediction of Student Performance

Authors:
raw_author_name: Abdulmalek Al-Gahmi
raw_affiliation_string: Weber State University
raw_author_name: Yong Zhang
raw_affiliation_string: Weber State University

Abstract: Abstract Learning Management Systems (LMS) gather extensive amounts of data about students' progression through courses. Such data is then made available via APIs for data exploration and utilization. A body of research has investigated using such data to predict student performance based on data collected earlier in a course. The driving question in such efforts has been whether student performance can be accurately predicted early enough to intervene and provide needed help. Two main issues have been pointed out in these attempts: the portability and robustness of these predictions. In this work-in-progress study, we introduce a new approach to exploring LMS data. Such an approach looks at the data as a set of time series each representing the progress of a student within a course. This study explores how students advance through courses over time and the variability of student performance between any two time points. It utilizes the LMS data (Canvas in this case) obtained from multiple Computer Science courses taught by different instructors in different formats (online and face-to-face) over three years in a public four-year university. Ways for summarizing and visualizing such data are discussed, and useful predictor features are extracted and used to build and evaluate predictive models of student performance at any time point. The study explores questions such as: How does the progress of passed and failed students differ in these courses? How early can student performance be accurately predicted? Can data collected from one course be used to predict the performance of students in another course by the same or a different instructor? Are student journeys through courses unique, or are there patterns that transcend students and courses?
Knowledge and Instance Mapping: architecture for premeditated interoperability of disparate data for materials

Authors:
raw_author_name: Jaleesia D. Amos
raw_affiliation_string: Center for the Environmental Implications of Nano Technology (CEINT), Durham, USA
raw_author_name: Zhao Zhang
raw_affiliation_string: Center for the Environmental Implications of Nano Technology (CEINT), Durham, USA
raw_author_name: Yuan Tian
raw_affiliation_string: Center for the Environmental Implications of Nano Technology (CEINT), Durham, USA
raw_author_name: Mark R. Wiesner
raw_affiliation_string: Center for the Environmental Implications of Nano Technology (CEINT), Durham, USA

Abstract: Abstract Predicting and elucidating the impacts of materials on human health and the environment is an unending task that has taken on special significance in the context of nanomaterials research over the last two decades. The properties of materials in environmental and physiological media are dynamic, reflecting the complex interactions between materials and these media. This dynamic behavior requires special consideration in the design of databases and data curation that allow for subsequent comparability and interrogation of the data from potentially diverse sources. We present two data processing methods that can be integrated into the experimental process to encourage pre-mediated interoperability of disparate material data: Knowledge Mapping and Instance Mapping. Originally developed as a framework for the NanoInformatics Knowledge Commons (NIKC) database, this architecture and associated methods can be used independently of the NIKC and applied across multiple subfields of nanotechnology and material science.
Lab Modules for Developing Practical Skills in a Python Scripting Course

Authors:
raw_author_name: Peng Li
raw_affiliation_string: East Carolina University
raw_author_name: Ciprian Popoviciu
raw_affiliation_string: East Carolina University

Abstract: Abstract To implement recommendations from our Industry Advisory Board, we switched from PHP to Python Scripting Language in the course Scripting for Information Technology. Python is easy to learn, easy to use and powerful. It is widely used in software development (Dev) and IT operations (Ops), artificial intelligence (AI), big data and cloud computing. Our students are eager to learn hands-on skills of using Python in real-world scenarios, which are not covered adequately in the textbook. In this project, we developed five supplemental lab modules and an online, virtual environment, which can be used by both face-to-face students and distance education students, to meet their increasing demand for hands-on exercises. The lab modules include 1) Python in Linux Automation (Ansible), 2) Using Python with NoSQL database system MongoDB, 3) Web Programming Environment with Python, 4) Using Python with Podman RESTful API and 5) Simulating an Internet-Connected Weather Sensor Using Python. A Canvas shell was created to host lab manuals, lab quizzes, and supporting material such as demo video and tutorial. The Linux-based virtual environment is portable and can be accessed by students anytime and anywhere to do labs. Some tasks in the labs are automatically graded on Canvas. Other tasks are manually graded. The lab modules were deployed in two sections taught by different instructors in spring 2022. An anonymous survey was conducted at the end of the semester to collect feedback from students. The instructors also provided their evaluations. We believe that the hands-on approach will help students better understand course topics and improve their practical skills of using Python in the real world.
Tuning atom-field interaction via phase shaping

Authors:
raw_author_name: Y.-T. Cheng
raw_affiliation_string: Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
raw_author_name: P. Y. Wen
raw_affiliation_string: Department of Physics, National Chung Cheng University, Chiayi 621301, Taiwan
raw_author_name: W.-J. Lin
raw_affiliation_string: Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
raw_author_name: Y. Lu
raw_affiliation_string: Department of Microtechnology and Nanoscience (MC2), Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
raw_author_name: F. Aziz
raw_affiliation_string: Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
raw_author_name: C.-Y. Chen
raw_affiliation_string: Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
raw_author_name: J. C. Chen
raw_affiliation_string: Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
raw_author_name: C.-S. Chuu
raw_affiliation_string: Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
raw_author_name: A. F. Kockum
raw_affiliation_string: Department of Microtechnology and Nanoscience (MC2), Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
raw_author_name: G.-D. Lin
raw_affiliation_string: Department of Physics and Center for Quantum Science and Engineering, National Taiwan University, Taipei 10617, Taiwan; Physics Division, National Center for Theoretical Sciences, Taipei 10617, Taiwan; Trapped-Ion Quantum Computing Laboratory, Hon Hai Research Institute, Taipei 11492, Taiwan
raw_author_name: Y.-H. Lin
raw_affiliation_string: Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
raw_author_name: I.-C. Hoi
raw_affiliation_string: Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China; Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan

Abstract: A coherent electromagnetic field can be described by its amplitude, frequency, and phase. All these properties can influence the interaction between the field and an atom. Here we demonstrate the phase shaping of microwaves that are scattered by a superconducting artificial atom coupled to the end of a semi-infinite one-dimensional transmission line. In particular, we input a weak exponentially rising pulse with phase modulation to a transmon qubit. We observe that atom-field interaction can be tuned from a nearly full interaction (interaction efficiency, i.e., amount of the field energy interacting with the atom, of 94.5 %) to effectively no interaction (interaction efficiency of 3.5 %).
Automated Detection and Machine Learning‐Based Classification of Seismic Tremors Associated With a Non‐Volcanic Gas Emission (Mefite d’Ansanto, Southern Italy)

Authors:
raw_author_name: S. Panebianco
raw_affiliation_string: National Research Council of Italy (CNR-IMAA), Tito Scalo, Italy; Now at Department of Physics and Earth Sciences, University of Ferrara, Ferrara, Italy
raw_author_name: C. Satriano
raw_affiliation_string: Institute of the Globe Physics, University of Paris, Paris, France
raw_author_name: G. Vivone
raw_affiliation_string: National Research Council of Italy (CNR-IMAA), Tito Scalo, Italy
raw_author_name: M. Picozzi
raw_affiliation_string: Department of Physics, University of Naples, Naples, Italy
raw_author_name: A. Strollo
raw_affiliation_string: GFZ German Research Centre for Geoscience, Potsdam, Germany
raw_author_name: T. A. Stabile
raw_affiliation_string: National Research Council of Italy (CNR-IMAA), Tito Scalo, Italy

Abstract: Abstract A major aim in the study of crustal fluids is the development of automatic methodologies for monitoring deep‐source, non‐volcanic gas emissions’ spatio‐temporal evolution. Crustal fluids play a significant role in the generation of large earthquakes and the characterization of their emissions on the surface can be essential for better understanding crustal processes generating earthquakes. We investigate seismic tremors recorded over 4 days in 2019 at the Mefite d’Ansanto (southern Apennines, Italy) that is located at the northern end of the fault system that generated the Mw 6.9 1980 Irpinia Earthquake. The Mefite d’Ansanto is hypothesized to be the largest natural, non‐volcanic, CO 2 ‐rich gas emission on Earth. The seismic tremor is studied by employing a dense temporary seismic network and an automated detection algorithm based on non‐parametric statistics of the recorded signal amplitudes. We extracted signal characteristics (RMS amplitude and statistical moments of amplitudes both in time and frequency domains) for use in the subsequent supervised machine‐learning classification of the target tremor and accidently detected anthropogenic and background noise. The data set is used for the training and optimization of station‐based KNN (k‐Nearest‐Neighbors) binary classifiers obtaining good classification performances with a median overall accuracy across all stations of 92.8%. The classified tremor displayed common features at all stations: variable duration (16 s to 30–40 min), broad peak frequency (4–20 Hz) with varying amplitudes, and two types of signals: (a) long‐duration, high‐amplitude tremor and (b) pulsating tremor. Higher tremor amplitudes recorded at stations closer to local bubbling and pressurized vents suggest multiple local tremor sources.
Offline Handwritten Signature Identification based on Hybrid Features and Proposed Deep Model

Authors:
raw_author_name: Zainab Hashim
raw_affiliation_string: Department of Computer Sciences, University of Technology
raw_author_name: Hanaa Mohsin
raw_affiliation_string: Department of Computer Sciences, University of Technology, Baghdad, IRAQ
raw_author_name: Ahmed Alkhayyat
raw_affiliation_string: Department of Computer Technical Engineering, College of Technical Engineering, Islamic University, Najaf, IRAQ

Abstract: Handwritten signature identification is the process of identifying the true identity of an individual by analyzing their signature. This is an important task in applications such as financial transactions, legal documents, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. This paper proposes an authentic signature identification method based on integrating static (off-line) signature data and proposed deep-based model, this is done by fused three types of signature features, Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, the fused features are inputted to the proposed deep-based model of 25 layers for identifying each person. For experiments, we have used three datasets: Our own private collected dataset, called SigArab, and two public datasets called SigComp11 and CEDAR respectively. The proposed deep model achieves 99.23%, 100%, 100% accuracy on SigArab, CEDAR and SigComp2011 datasets.
Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

Authors:
raw_author_name: Zhanyang Xu
raw_affiliation_string: School of Software Nanjing University of Information Science &amp; Technology Nanjing China
raw_author_name: Hongyan Shi
raw_affiliation_string: School of Software Nanjing University of Information Science &amp; Technology Nanjing China
raw_author_name: Jiarui Zhang
raw_affiliation_string: School of Computer Science Nanjing University of Information Science &amp; Technology Nanjing China
raw_author_name: Wei Lin
raw_affiliation_string: Nanjing Technology R&amp;D Center of Jiangsu Children's Spring Interconnection Education Technology Co. Nanjing China

Abstract: Abstract The Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.
LoHDP: Adaptive local differential privacy for high‐dimensional data publishing

Authors:
raw_author_name: Guohua Shen
raw_affiliation_string: Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Safety-Critical Software, Ministry of Industry and Information Technology, Nanjing, China
raw_author_name: Mengnan Cai
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Zhiqiu Huang
raw_affiliation_string: Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Safety-Critical Software, Ministry of Industry and Information Technology, Nanjing, China
raw_author_name: Yang Yang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Feifei Guo
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Linlin Wei
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract: Summary The increasing availability of high‐dimensional data collected from numerous users has led to the need for multi‐dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low‐dimensional marginals of these clusters, allowing for an approximation of the distribution of the original dataset and the generation of synthetic datasets. Existing solutions have limitations in computing the marginals of pairwise attributes and multi‐dimensional distribution on attribute clusters, as well as constructing relational dependency graphs that contain large clusters. To address these problems, we propose LoHDP, a high‐dimensional data publishing method composed of adaptive marginal computing and an effective attribute clustering method. The adaptive local marginal calculates any k ‐dimensional marginals required in the algorithm. In particular, methods such as sampling‐based randomized response are used instead of privacy budget splits to perturb user data. The attribute clustering method measures the correlation between pairwise attributes using an effective method, reduces the search space during the construction of the dependency graph using high‐pass filtering technology, and realizes dimensionality reduction by combining sufficient triangulation operation. We demonstrate through extensive experiments on real datasets that our LoHDP method outperforms existing methods in terms of synthetic dataset quality.
Research on Governance of Higher Engineering Education Quality in China after Accessing the Washington Accord

Authors:
raw_author_name: Ming Li
raw_affiliation_string: Beijing Foreign Studies University
raw_author_name: Min Zhao
raw_affiliation_string: Graduate School of Education, Beijing Foreign Studies University, Beijing, China.

Abstract: Abstract As an important quality assurance mechanism, the program accreditation of engineering education has become the basic mechanism for improving the quality of engineering education in various countries. Under the guidance of the CPC Central Committee and the State Council, China has established quality assurance mechanism of engineering education. In June 2016, China officially accessed the Washington Accord, which symbolized that its engineering education accreditation system has been recognized by the international engineering community. However, China's accessing the Washington Accord as a full signatory is only the beginning of integrating into the international engineering community. China must actively respond to the contrasts with international engineering education, and optimize the accreditation system of engineering education. Thus, this research attempts to propose an emerging concept, i.e., governance of higher engineering education quality; draw on the Governance Theory to establish a logical framework for the governance of higher engineering education quality in China around the dimensions of governance motivation, subjects, objects, purposes and approaches, so as to provide "China Solution" in the context of global quality assurance in engineering education.