Reviewerly/exHarmony huggingface dataset:
BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared Language Model

Binary authorship analysis is a significant problem in many software engineering applications. In this paper, we formulate a binary authorship verification task to accurately reflect the real-world working process of software forensic experts. It aims to determine whether an anonymous binary is developed by a specific programmer with a small set of support samples, and the actual developer may not belong to the known candidate set but from the wild. We propose an effective binary authorship verification framework, BinMLM. BinMLM trains the RNN language model on consecutive opcode traces extracted from the control-flow-graph (CFG) to characterize the candidate developers' programming styles. We build a mixture-of-shared architecture with multiple shared encoders and author-specific gate layers, which can learn the developers' combination preferences of universal programming patterns and alleviate the problem of low training resources. Through an optimization pipeline of external pre-training, joint training, and fine-tuning, our framework can eliminate additional noise and accurately distill developers' unique styles. Extensive experiments show that BinMLM achieves promising results on Google Code Jam (GCJ) and Codeforces datasets with different numbers of programmers and supporting samples. It significantly outperforms the baselines built on the state-of-the-art feature set (4.73% to 19.46% improvement) and remains robust in multi-author collaboration scenarios. Furthermore, BinMLM can perform organization-level verification on a real-world APT malware dataset, which can provide valuable auxiliary information for exploring the group behind the APT attack.
Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network

With the development of Internet technology, government affairs can be handled online. More and more citizens are using online platforms to report to government departments, which is generating a lot of textual data. Among them, the basic but important problem is to automatically classify the different categories of messages, so that staff from different departments can process relevant information quickly. However, government messages have problems such as fast update rate, a large amount of information, long texts, and difficulty in capturing key points, which make supervised learning methods unsuitable for processing such texts. To address these problems, we propose a semisupervised text classification method based on a transformer-based pointer generator network named Ptr4BERT, which uses the pointer generator network with BERT(bidirectional encoder representation from transformers) embedding as a preprocessor for feature extraction. In this method, text classification can achieve very good results with a small set of labeled data, by extracting features exclusively from the message text. In order to verify the effect of our proposed model, we performed some experiments. Besides, we designed a crawler program and obtained two datasets from different websites, which are named HNMes and QDMes. Experimental results have shown that the proposed method outperforms the state-of-the-art methods significantly.
On confidence intervals for precision matrices and the eigendecomposition of covariance matrices

The eigendecomposition of a matrix is the central procedure in probabilistic models based on matrix factorization, for instance principal component analysis and topic models. Quantifying the uncertainty of such a decomposition based on a finite sample estimate is essential to reasoning under uncertainty when employing such models. This paper tackles the challenge of computing confidence bounds on the individual entries of eigenvectors of a covariance matrix of fixed dimension. Moreover, we derive a method to bound the entries of the inverse covariance matrix, the so-called precision matrix. The assumptions behind our method are minimal and require that the covariance matrix exists, and its empirical estimator converges to the true covariance. We make use of the theory of U-statistics to bound the $L_2$ perturbation of the empirical covariance matrix. From this result, we obtain bounds on the eigenvectors using Weyl's theorem and the eigenvalue-eigenvector identity and we derive confidence intervals on the entries of the precision matrix using matrix inversion perturbation bounds. As an application of these results, we demonstrate a new statistical test, which allows us to test for non-zero values of the precision matrix. We compare this test to the well-known Fisher-z test for partial correlations, and demonstrate the soundness and scalability of the proposed statistical test, as well as its application to real-world data from medical and physics domains.
Adversarial Bayesian Simulation

In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference. Our work bridges ABC with deep neural implicit samplers based on generative adversarial networks (GANs) and adversarial variational Bayes. Both ABC and GANs compare aspects of observed and fake data to simulate from posteriors and likelihoods, respectively. We develop a Bayesian GAN (B-GAN) sampler that directly targets the posterior by solving an adversarial optimization problem. B-GAN is driven by a deterministic mapping learned on the ABC reference by conditional GANs. Once the mapping has been trained, iid posterior samples are obtained by filtering noise at a negligible additional cost. We propose two post-processing local refinements using (1) data-driven proposals with importance reweighting, and (2) variational Bayes. We support our findings with frequentist-Bayesian results, showing that the typical total variation distance between the true and approximate posteriors converges to zero for certain neural network generators and discriminators. Our findings on simulated data show highly competitive performance relative to some of the most recent likelihood-free posterior simulators.
Electronic Appendix to "Input Invariants"

In this electronic appendix to our paper "Input Invariants," accepted at ESEC/FSE'22, we provide additional examples, formal definitions, theorems, and proof sketches to complement our paper. Furthermore, we show the invariants that ISLearn mined in our evaluation.
A derivation of variational message passing (VMP) for latent Dirichlet allocation (LDA)

Latent Dirichlet Allocation (LDA) is a probabilistic model used to uncover latent topics in a corpus of documents. Inference is often performed using variational Bayes (VB) algorithms, which calculate a lower bound to the posterior distribution over the parameters. Deriving the variational update equations for new models requires considerable manual effort; variational message passing (VMP) has emerged as a "black-box" tool to expedite the process of variational inference. But applying VMP in practice still presents subtle challenges, and the existing literature does not contain the steps that are necessary to implement VMP for the standard smoothed LDA model, nor are available black-box probabilistic graphical modelling software able to do the word-topic updates necessary to implement LDA. In this paper, we therefore present a detailed derivation of the VMP update equations for LDA. We see this as a first step to enabling other researchers to calculate the VMP updates for similar graphical models.
Sorsogon Legends in a Comic Book form: An Ginikanan

This paper is anchored on the idea that prose narratives, specifically legends, are a rich source of people’s cultural background that the younger generation needs to understand. These narratives were presented in a comic book format as instructional material in the classroom to encourage learners’ reading interest for further learning. This is descriptive research that utilized a combination of quantitative and qualitative methods. The focus is to identify which legends could be used as reading materials in a form of comics. This paper evaluated the level of acceptability of the developed material and identified the teachers’ strategies. This paper found out that there were six legends that passed the criteria. The respondents rated the instructional material or the legends in comics form along with the areas of suitability or appropriateness to the pupils, physical aspect, or visual appeal, both as very much acceptable, while the quality of the stories as moderately acceptable. The researchers recommended further innovations on the use of prose narratives for posterity. Contextualization may also encourage learning with cultural identity. Keywords: Sorsogon Legends, Comic book, An Ginikanan, Instructional Materials
Graph integration of structured, semistructured and unstructured data for data journalism

Nowadays, journalism is facilitated by the existence of large amounts of digital data sources, including many Open Data ones. Such data sources are extremely heterogeneous, ranging from highly struc-tured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to de ne and deploy custom extract-transform-load work ows. These are di cult to set up not only for arbitrary heterogeneous inputs , but also given that users may want to add (or remove) datasets to (from) the corpus. We describe a complete approach for integrating dynamic sets of heterogeneous data sources along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.
Detecting Forged Images using Deep Learning

This availability and requirement of data calls for the credibility and authenticity of the data. One such domain is images where tampering creates concern , leading to wide spread of misinformation and fake news. Images are transferred to initiate propagandas on social handles and other platforms. Most of these images are tampered from the authentic original content to allude people and miscommunicate malicious information. In this application, our main work is to modify the existing MobileNetV2 family of neural networks to a more relevant version, so that we can identify and differentiate tampered images from authentic images. We will further create our own convolutional neural network, to create an application which can help us to identify and differentiate tampered images from authentic images and compare our model with MobileNetV2.
The Homework Model of Screening English Teaching Courses Based on Fractional Differential Equations

Abstract The phenomenon of plagiarism in coursework is common among college students. This phenomenon seriously affects the quality of higher education. The school has developed a series of homework similarity checking models to ensure the originality of English teaching coursework. The fractional differential equation method is used to classify and analyze the assignments with plagiarism suspicion. The article calculates program similarity’s fractional differential equation calculation by sequence matching algorithm in English homework. Finally, we extract AST features of similar program parts and generate spatial vectors. The research aims to find out plagiarized assignment texts by cluster analysis. Experimental research shows that the establishment of this model can assist English teachers in checking online homework. At the same time, the model can also help improve the efficiency of teachers’ homework checking.
Geochemistry study of black shale in Barzewa and Warte Sections, Sargelu Formation, Kurdistan Region, Iraq

Sargelu Formation (Middle Jurassic) is characterized by a wide geographical distribution in Iraq and neighboring countries. This study includes two sections Barzewa and Warte locations in Erbil Governorate of Kurdistan region, Iraq, the organic, inorganic Geochemistry and mineralogical properties were recorded. Fifteen samples of Sargelu Formation black shales were gathered from both locations. The source rocks of the black shales have an intermediate to felsic composition. The illite crystallinity index (IC) of the studied black shale shows the high anchizone stage. The organic geochemical parameters were analyzed in Sargelu Formation, the black shale contains type II - III kerogen. The average total organic carbon (TOC) of black shale for both section is 4.29 wt.%. By using the ultrasound homogenizer process the C-H bond (hydrocarbon component) of black shale recovered from the solid state to the liquid state.
Design Space Exploration of DICE

Trusted Computing aims to secure computer systems by ensuring that only trusted software is executed on the system, so that it behaves in expected ways. One of the approaches to this concept is the Device Identifier Composition Engine (DICE), which is specified by the Trusted Computing Group (TCG) as a solution for resource-limited devices. DICE is supposed to be a Root of Trust, which enables the implementation of a Chain of Trust on the device. It is designed for off-the-shelf hardware, such that it can be used on most modern micro-controllers. Therefore, it needs to be as minimal on resource usage as possible. Implementations until now were either focused on extending DICE with new concepts or implementing DICE with as little hardware as possible. Also they usually only implemented DICE on one single device. These factors limit the significance of their results for general DICE implementations as they mostly concentrate on evaluating their extended concepts and specific implementation features. This paper aims for the contrary, focusing more on general configuration and implementation details applicable to most DICE implementations than on specific aspects. We evaluated many different configurations for multiple devices and used these to give suggestions on possible configurations for different use cases. DICE is commonly used as he foundation to create a Chain of Trust, where firmware components get executed in sequential order. Usually a key generation component is used to generate purpose-bound keys after DICE, but specifics are application-dependent. We also implemented this component and to evaluate its key generation for different key configurations. Additionally, we implemented an example of Remote Attestation to show how the DICE architecture can be used.
Clickable Object Detection Network for a Wide Range of Mobile Screen Resolutions

Recently, as the development cycle of applications has been shortened, it is important to develop rapid and accurate application testing technology. Since application testing requires a lot of cost, mobile component detection technology using deep learning is essential to prevent the use of expensive human resources. In this paper, we shall propose a Clickable Object Detection Network (CODNet) for mobile component detection in a wide range of mobile screen resolutions. CODNet consists of three modules: feature extraction, deconvolution and prediction modules in order to provide performance improvement and scalability. The Feature Extraction module uses squeeze and excitation blocks to efficiently extract features and change the ratio of the input image to 1:2 most close to that of mobile screen. Deconvolution module provides feature map of various sizes by upsampling feature map through top-down pathway and lateral connections. The prediction module selects an anchor size suitable for the mobile environment using the Anchor Transfer block, among the set of anchor candidates obtained through the analysis of mobile dataset. Moreover, we shall improve object detection performance by building a new mobile screen dataset consisting of data collected from various resolutions and operating systems. We shall show that our model achieves competitive performance in mean average precision on our dataset compared to the other models.
Scrambling additive lagged-Fibonacci generators

Abstract Random numbers are used in a variety of applications including simulation, sampling, and cryptography. Fortunately, there exist many well-established methods of random number generation. An example of a well-known pseudorandom number generator is the lagged-Fibonacci generator (LFG). Marsaglia showed that the lagged-Fibonacci generator using addition failed some of his DIEHARD statistical tests, while it passed all when longer lags were used. This paper presents a scrambler that takes bits from a pseudorandom number generator and outputs (hopefully) improved pseudorandom numbers. The scrambler is based on a modified Feistel function, a method used in the generation of cryptographic random numbers, and multiplication by a chosen multiplier. We show that this scrambler improves the quality of pseudorandom numbers by applying it to the additive LFG with small lags. The scrambler performs well based on its performance with the TestU01 suite of randomness tests. The TestU01 suite of randomness tests is more comprehensive than the DIEHARD tests. In fact, the specific suite of tests we used from TestU01 includes the DIEHARD tests The scrambling of the LFG is so successful that scrambled LFGs with small lags perform as well as unscrambled LFGs with long lags. This comes at the cost of a doubling of execution time, and provides users with generators with small memory footprints that can provide parallel generators like the LFGs in the SPRNG parallel random number generation package.
Augmented Reality in Language Learning: Enhancing Student Engagement in a Third Language Subject

To improve student performance on the subjects studied, the fields of education and technology cannot be separated.Although technology has been used in Third Language Subject Education for a long time, student performance has been seen to decline from 2020 to 2021.In fact, the use of augmented reality (AR) multimedia technology continues to remain uncommon, particularly in third-language subjects at Universiti Teknologi MARA (UiTM).Due to the paucity of studies on the impact of using AR technology in the third language on student performance, this needs to be investigated.The use of AR in education and training can speed up instruction, be lightweight and portable, have clear content, and cost less to print.The purpose of this research is to determine how the use of AR-oriented multimedia technology affects student performance in third-language learning methods and lecturer roles.Data from questionnaires is collected using quantitative methods.SmartPLS 3.0 was used to analyse the data.A total of 387 UiTM students who took a third language course participated in the study.The analysis revealed that the use of multimedia had a significant impact on student performance, with the exception of the teaching strategy factor, which had no effect on student performance.Future proposals to broaden AR research into other areas of social science.
The Impact of Ez-Article Software on Post Graduate Students Academic Writing: A Tracking Study

Student achievement and professional success depend on students' abilities in academic writing.In the higher education institutions, students are tested on their writing abilities by completing academic writing written in a variety of writing styles.This EzArticle software was created to help students and researchres get better at writing, better arrange facts, and better organise data used in academic writing.It was designed to be a quick, simple, and userfriendly method for organising the data collected in the academic literature, which may then be used in writing.The programme also facilitates the tabular presentation of data and concepts, which may be easily sorted and reorganised to meet the needs of a variety of situations and projects.In this study, 280 software end-users were polled using Google survey, and a few software end-users were interviewed.The numeric data was examined using SPSS, while the qualitative interview data was studied using a content analysis approach.Overall, the survey found that the majority of respondents give very positive feedback.The study also showed that the majority of responses exceeded 80% agreements.The qualitative results demonstrate, however, that everyone who has used the programme agrees that it has significantly improved their writing and made it more organised.
The Management System for the Healthcare Resources: A review

Recently, healthcare resources and services can be effectively provided to patients anytime and anywhere using healthcare management systems. Healthcare systems are developed through many mechanisms and applications that improve the management of healthcare-related resources by aggregating data with mechanisms such as the Internet of Things (IoT) that include sensors and mobile phones, as well as using unified databases for such resources so that they are arranged and managed in light of what is available and what is required and determine the required delivery to the requesting party as soon as possible. Healthcare and services are indicating many things related to health such as kidney donors, surgeries, advice from doctors, etc. Here in our study, we will use the blood banks as the sample of the services that the management system can provide. Today, the application of modern technologies such as cloud programming and real-time algorithms has helped solve many problems associated with traditional systems. Real-time algorithms are concerned with producing logically accurate results and answers in a timely manner without human intervention. Telehealth is an important purpose of the healthcare system where device interoperability and data normalization are challenging task that requires research attention. Several solutions have been proposed in the literature based on manual interpretation through explicit programming. However, programmatic implementation of the interpretation of data sender and receiver in a healthcare system for data transmission is counterproductive as modification is required for each new device added to the system. In this paper, some research works on managing the healthcare system and facilitating their work using modern technology have been studied and analyzed.
Analysis of Language Usage Among College Students in National “Double First-class” Agricultural Universities—A Case Study of Northwest A&amp;F University

Young students are the main force of the country's future development, and their language ability plays an important role in the direction of China's future language development. At present, most domestic studies focus on the language use situation of minority groups, the language use situation of youth groups, and less on the language use situation of university undergraduates. This study takes the undergraduate students of Northwest Agriculture and Forestry University (NWAFU) of Science and Technology as the subjects of investigation, and explores their language life situation, intending to find answers to the inherited language vitality from the perspective of young college students.