Reviewerly/exHarmony huggingface dataset:
A classification approach for software requirements towards maintainable security

As trends continue to move toward the introduction of intelligent methods to automate software engineering processes, security requirements classification is rapidly turning into a highly potent field for the software engineering community. There are several models for classifying security requirements proposed in the literature. However, their adoption and use is constrained by the absence of substantial datasets to allow for the replication and generalization of studies, using more advanced machine-learning techniques. Furthermore, most researchers in this area, consider Maintainability as purely a non-functional requirement with no relation to security. This has been identified to be a major source of security concerns. The main objective of this study is to propose a software requirements classification approach that considers maintainability as a security requirement. This seeks to ensure that maintenance efforts don't lead to new software vulnerabilities that were previously not present during deployment. A mixed research methodology is adopted as qualitative data is collected from students’ project documentation, labelled, and transformed into quantitative form during analysis. As a culmination of this process, a validated original publicly accessible, labelled software requirements dataset of student software project requirements (DOSSPRE) is obtained and presented to support the approach. It contains 1317 software requirements, including security requirements, functional requirements, and other non-functional requirements. Two versions of the dataset are presented: one for binary classification of security requirements vs. non-security requirements and the other for multi-class classification tasks with various more granular security requirements vs. non-security requirements. In both instances, well-known machine learning algorithms are used to verify the dataset. Support Vector Machine (SVM) and Logistic Regression were the top performers in multi-class classification with an average Accuracy of 86% in both cases. Multinomial Nave Bayes topped the other machine learning techniques in binary classification with 91% Precision, 69% Recall, 78% F1-Score, and Accuracy of 86%. The dataset is accessible on this link https://data.mendeley.com/datasets/23xtbvk6yp/1
FUZZY BASED REGION GROWING SEGMENTATION WITH MODIFIED LEAST SQUARE FITTING ALGORITHM FOR AUTOMATIC FETAL GROWTH ESTIMATION

During pregnancy monitoring, the fetal ultrasound image can provide valuable diagnostic information. Due to low soft tissue contrast, speckle, and shadowing effects, manual segmentation of subcortical structures in ultrasonic volumes is time-consuming and difficult. For this reason, we developed Discrete Wavelet Transform (DWT) based fuzzy region growing segmentation scheme. Considering the aforementioned facts, the present study deals with automatic estimation of gestation age using the head circumference data obtained/ retrieved from the ultrasonic images of the fetus. The proposed work utilizes the DWT based anisotropic diffusion filtering method to enhance the de-noised image resolution. Further implementation of genetic algorithm based Least Square Fitting (LSF) algorithm to find bi-parietal distance (BPD) and head circumference (HC) which will enable the calculate of gestation age. The fetal age is automatically estimated using K-Nearest Neighbour (KNN) Classifier from the standard reference values. By deploying fuzzy based region growing segmentation technique this study attempts to aid unique and effective segmentation process. Apart from highlighting the uniqueness of this method, from other existing techniques, this study elaborately discusses the steps and mechanisms involved in various stages of image processes starting from effective denoising to fetal age determination.
3D object detection using improved PointRCNN

Recently, two-dimensional object detection (2D object detection) has been introduced in numerous applications such as building exterior diagnosis, crime prevention and surveillance, and medical fields. However, the distance (depth) information is not enough for indoor robot navigation, robot grasping, autonomous running, and so on, with conventional object detection. Therefore, in order to improve the accuracy of 3D object detection, this paper proposes an improvement of Point RCNN, which is a segmentation-based method using RPNs and has performed well in 3D detection benchmarks on the KITTI dataset commonly used in recognition tasks for automatic driving. The proposed improvement is to improve the network in the first stage of generating 3D box candidates in order to solve the problem of frequent false positives. Specifically, we added a Squeeze and Excitation (SE) Block to the network of pointnet++ that performs feature extraction in the first stage and changed the activation function from ReLU to Mish. Experiments were conducted on the KITTI dataset, which is commonly used in research aimed at automated driving, and an accurate comparison was conducted using AP. The proposed method outperforms the conventional method by several percent on all three difficulty levels.
„Ich lasse mir nicht den Mund verbieten”: Wie vorpositionieren sich Impfgegner sprachlich und multimodal? Textorientierte Analyse von ausgewählten Selbstdarstellungsmodulen in deutschsprachigen sozialen Medien am Beispiel von Twitter-UserInnen „I Will Not Be Banned From Speaking”: How Opponents of Vaccination Pre-position Themselves Linguistically and Multimodally? A Text-Oriented Analysis of Selected Self-Representation Modules in German-Language Social Media Using the Example of Twitter Users

No Access„Ich lasse mir nicht den Mund verbieten”: Wie vorpositionieren sich Impfgegner sprachlich und multimodal? Textorientierte Analyse von ausgewählten Selbstdarstellungsmodulen in deutschsprachigen sozialen Medien am Beispiel von Twitter-UserInnen „I Will Not Be Banned From Speaking”: How Opponents of Vaccination Pre-position Themselves Linguistically and Multimodally? A Text-Oriented Analysis of Selected Self-Representation Modules in German-Language Social Media Using the Example of Twitter UsersPaweł RybszlegerPaweł Rybszlegerhttps://orcid.org/0000-0003-4369-1377Search for more papers by this authorhttps://doi.org/10.14220/9783737015127.217SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1. AuflageISBN: 978-3-8471-1512-0 eISBN: 978-3-7370-1512-7HistoryPublished online:December 2022 PDF download
On the Generation of Desired Outputs for Spike Neural Networks (SNN)

In supervised learning algorithms, it is necessary to define an error function for the parameter adjustment process to take place. This function generation requires the input feature vectors and their respective desired outputs. In the context of neural networks, the network outputs are compared with the desired outputs so as to compute the error function. For standard networks, such as Perceptron, Adaline and others, the desired outputs are basically a class label or output value that will be directly used to calculate the network error. In the case of bioinspired networks, such as those using Leaky Integrate-and-Fire (LIF) neurons, their output are electrical impulses (spikes). In such cases, the electrical impulse has a built-in temporal dependence that does not occur for Perceptron neurons, thus representing a challenge to calculate the desired output values (spikes) for Spike Neural Networks (SNN). The purpose of this paper is to define an analytical solution to build the desired spikes for each category in a classification problem for a SNN. The computational challenge encountered to represent the dynamics of spike generation in bioinspired neurons will also be discussed, which has a direct impact on the objective of the proposed solution.
Foundational patterns benchmark

Recently, there has been growing interest in the use of ontology as a fundamental tool for representing domain-specific conceptual models to improve the semantics, accuracy, and relevance of domain users’ query results. Although the amount of data has grown steadily over the past decade, much data shares similar characteristics that can be captured by a foundational ontology. In this paper, we show how queries based on a foundational ontology can be evaluated and their performance measured. We also present a Foundational Patterns benchmark to help select the most efficient triple memory and its layout. We evaluate the foundational benchmark with both generated and real datasets for state-of-the-art triple stores.
Graph Attention Interaction Aggregation Network for Click-Through Rate Prediction

Click-through rate prediction is a critical task for computational advertising and recommendation systems, where the key challenge is to model feature interactions between different feature domains. At present, the main click-through rate prediction models model feature interactions in an implicit way, which leads to poor interpretation of the model, and the interaction between each pair of features may introduce noise into the model, thus limiting the predictive ability of the model. In response to the above problems, this paper proposes a click-through rate prediction model (GAIAN) based on the graph attention interactive aggregation network, which explicitly obtains cross features on the graph structure. Our specific method is to design a feature interactive selection mechanism to select cross features that are beneficial to model prediction, reducing model noise and reducing the risk of model overfitting. On this basis, the bilinear interaction function is integrated into the aggregation strategy of the graph neural network, and the fine-grained intersection features are extracted in a flexible and explicit way, which makes graph neural networks more suitable for modeling feature interactions and enhances the interpretability of the model. Compared with several other state-of-the-art models on the Criteo and Avazu datasets, the experimental results show the superiority of the model.
BiVaSE: A bilingual variational sentence encoder with randomly initialized Transformer layers

Abstract Transformer-based NLP models have achieved state-of-the-art results in many NLP tasks including text classification and text generation. However, the layers of these models do not output any explicit representations for texts units larger than tokens (e.g. sentences), although such representations are required to perform text classification. Sentence encodings are usually obtained by applying a pooling technique during fine-tuning on a specific task. In this paper, a new sentence encoder is introduced. Relying on an autoencoder architecture, it was trained to learn sentence representations from the very beginning of its training. The model was trained on bilingual data with variational Bayesian inference. Sentence representations were evaluated in downstream and linguistic probing tasks. Although the newly introduced encoder generally performs worse than well-known Transformer-based encoders, the experiments show that it was able to learn to incorporate linguistic information in the sentence representations.
The Bookkeeping System Policy and Literacy Movement in Indonesia

The paper aims to reveal the book system policy in Indonesia and the efforts made to increase interest in reading, including the movement undertaken to improve literacy in Indonesia. Identification and mapping have been executed through the regulatory survey method on the peraturan.go.id database, while slip and analysis have been exercised through content analysis. The paper provides empirical insights into the bookkeeping system and movement undertaken to improve literacy in Indonesia. National policies have supported the bookkeeping system in Indonesia. Meanwhile, to increase interest in reading, a literacy movement was initiated by President Susilo Bambang Yudhoyono on November 1, 2007, through the Law of the Republic of Indonesia Number 43 of 2007. Indonesia's literacy movement is "the national movement for reading fondness." This paper reveals that policy support at the national (state) level also needs to get help at the regional (local) level so that the literacy movement can be implemented up to the regional level. In addition, the central government's commitment is not only in the form of policy support but needs to be followed by several joint movements and incentives.
The Diagnosis of Congestive Heart Failure Based on Generalized Multiscale Entropy-Wavelet Leaders

Congestive heart failure (CHF) is a chronic heart condition associated with debilitating symptoms that can lead to mortality. The electrocardiogram (ECG) is a noninvasive and simple diagnostic method that can show detectable changes in CHF. However, manual diagnosis of ECG signals is often erroneous due to the small amplitude and duration of the ECG signals. This paper presents a CHF diagnosis method based on generalized multiscale entropy (MSE)-wavelet leaders (WL) and extreme learning machine (ELM). Firstly, ECG signals from normal sinus rhythm (NSR) and congestive heart failure (CHF) patients are pre-processed. Then, parameters such as segmentation time and scale factor are chosen, and the multifractal spectrum features and number of ELM hidden layer nodes are determined. Two different data sets (A, B) were used for training and testing. In both sets, the balanced data set (B) had the highest accuracy of 99.72%, precision, sensitivity, specificity, and F1 score of 99.46%, 100%, 99.44%, and 99.73%, respectively. The unbalanced data set (A) attained an accuracy of 99.56%, precision of 99.44%, sensitivity of 99.81%, specificity of 99.17%, and F1 score of 99.62%. Finally, increasing the number of ECG segments and different algorithms validated the probability of detection of the unbalanced data set. The results indicate that our proposed method requires a lower number of ECG segments and does not require the detection of R waves. Moreover, the method can improve the probability of detection of unbalanced data sets and provide diagnostic assistance to cardiologists by providing a more objective and faster interpretation of ECG signals.
TESL Undergraduates’ Perceptions: Utilizing Social Media to Elevate Speaking Skills

Teaching English as a Second Language undergraduates are English teachers in training. Recently, a concern arose when it was found that these undergraduates in Indonesia perceived their speaking skills as the lowest in terms of their mastery. Many studies have been conducted on the use of social media. Still, there is a literature gap in using social media among TESL undergraduates in the Malaysian context. Thus, this study aimed to explore TESL undergraduates’ perceptions of their speaking skill mastery and their perceptions of utilizing social media to enhance their speaking skills. It was significant to investigate how those undergraduates feel about their speaking skill mastery to create self-awareness. By being aware of their belief, they can find ways to keep improving themselves. The main question for the study was to discover respondents’ belief in their speaking mastery level and respondents’ self-confidence in speaking performance. A total of 132 respondents from the National University of Malaysia participated in this research through an online questionnaire conducted on Google Forms. The results showed that the students recorded unsatisfactory perceptions of their speaking skill mastery. The data revealed that social media does help in elevating their speaking skills. The students chose ‘WhatsApp,’ ‘YouTube,’ and ‘TikTok’ as their preferred social media that help their oral communication skills. In addition, limitations and recommendations were explained to better future works in the equivalent or relevant studies.
Lost Origin and Beyond. Towards a design framework for mixed reality theatrical experiences

This paper documents the origin and development of&amp;nbsp;Lost Origin,&amp;nbsp;an immersive experience taking place in mixed reality, designed by the production company Factory 42. Building on key findings from the experience, as well as Richard Schechner’s well-known&amp;nbsp;Six Axioms for Environmental Theater, the paper proposes an original framework for the design of immersive experiences in mixed reality.
Stopping Silent Sneaks: Defending against Malicious Mixes with Topological Engineering

Mixnets provide strong meta-data privacy and recent academic research and industrial projects have made strides in making them more secure, performant, and scalable. In this paper, we focus our work on stratified Mixnets, a popular design with real-world adoption. We identify and measure significant impacts of practical aspects such as: relay sampling and topology placement, network churn, and risks due to real-world usage patterns. We show that, due to the lack of incorporating these aspects in design decisions, Mixnets of this type are far more susceptible to user deanonymization than expected. In order to reason about and resolve these issues, we model Mixnets as a three-stage “Sample-Placement-Forward” pipeline and develop tools to analyze and evaluate design decisions. To address the identified gaps and weaknesses we propose Bow-Tie, a design that mitigates user deanonymization through a novel adaption of Tor’s guard design with an engineered guard layer and client guard-logic for stratified mixnets. We show that Bow-Tie has significantly higher user anonymity in the dynamic setting, where the Mixnet is used over a period of time, and is no worse in the static setting, where the user only sends a single message. We show the necessity of both the guard layer and client guard-logic in tandem as well as their individual effect when incorporated into other reference designs. We develop and implement two tools, 1) a mixnet topology generator (Mixnet-Topology-Generator (MTG)) and 2) a path simulator and security evaluator (routesim) that takes into account temporal dynamics and user behavior, to assist our analysis and empirical data collection. These tools are designed to help Mixnet designers assess the security and performance impact of their design decisions.
Making Memory Account Accountable: Analyzing and Detecting Memory Missing-account bugs for Container Platforms

Linux kernel introduces the memory control group (memcg) to account and confine memory usage at the process-level. Due to its flexibility and efficiency, memcg has been widely adopted by container platforms and has become a fundamental technique. While being critical, memory accounting is prone to missing-account bugs due to the diverse memory accounting interfaces and the massive amount of allocation/free paths. To our knowledge, there is still no systematic analysis against the memory missing-account problem, with respect to its security impacts, detection, etc.
A Qualitative Evaluation of Reverse Engineering Tool Usability

Software reverse engineering is a challenging and time consuming task. With the growing demand for reverse engineering in vulnerability discovery and malware analysis, manual reverse engineering cannot scale to meet the demand. There has been significant effort to develop automated tooling to support reverse engineers, but many reverse engineers report not using these tools. In this paper, we seek to understand whether this lack of use is an issue of usability. We performed a iterative open coding of 288 reverse engineering tools to identify common input and output methods, as well as whether the tools adhered to usability guidelines established in prior work. We found that most reverse engineering tools have limited interaction and usability support. However, usability issues vary between dynamic and static tools. Dynamic tools were less likely to provide easy-to-use interfaces, while static tools often did not allow reverse engineers to adjust the analysis. Based on our findings, we give recommendations for reverse engineering framework developers and suggest directions for future HCI research in reverse engineering.
User Perceptions of Five-Word Passwords

Human-chosen passwords are often short, selected non-uniformly, and thus, susceptible to automated guessing attacks. To help users to select more secure but memorable passwords, experts have recommended the use of passphrases of multiple words or phrases. In this paper, we explore a strategy for passphrase selection, so-called five-word passwords, where users are assigned five random words for a passphrase. Such a password composition policy was recently adopted at Georgetown University in December 2020. Through a two-part online survey (n = 150 and n = 116), participants selected a five-word password under different conditions. We find that computer-generated five-word passwords are more diverse and likely more secure than five-word passwords users select themselves. While all cases of five-word passwords are likely more secure than a human-generated, traditional password, participants expressed misconceptions regarding the security of five-word passwords (and passwords generally). Five-word passwords also appear to negatively impact usability, only 39.7 % of participants successfully recalled their password after two weeks. While five-word passwords offer improvements for security, more outreach is needed to explain their security benefits and reduce usability burdens.
Designing a Provenance Analysis for SGX Enclaves

SGX enclaves are trusted user-space memory regions that ensure isolation from the host, which is considered malicious. However, enclaves may suffer from vulnerabilities that allow adversaries to compromise their trustworthiness. Consequently, the SGX isolation may hinder defenders from recognizing an intrusion. Ideally, to identify compromised enclaves, the owner should have privileged access to the enclave memory and a policy to recognize the attack. Most importantly, these operations should not break the SGX properties.
MProbe: Make the code probing meaningless

Modern security methods use address space layout randomization (ASLR) to defend against code reuse attacks (CRAs). However, code probing can still obtain the content and address of the code through code probing. Code probing invalidates the widely used ASLR methods, causing researchers to lose confidence in them. On the contrary, we believe the ASLR is still effective, if it has anti-probing capability. To enhance the anti-probing capability of ASLR and defense CRAs, this paper proposes an anti-probing method MProbe. First, it detects the code probing activities of attackers, including address probing and content probing. Next, the execution permission of the probed code will be de-enabled in the original address space. At the same time, the equivalent code block in a random address space will replace the probed code. Finally, new security strategies are used to prevent the probed code blocks from being used as gadgets. Experiments and analysis show that MProbe has a good defense effect against CRAs based on code probing, and only introduces less than 3% performance overhead to the operating system (OS).
ZeroDNS: Towards Better Zero Trust Security using DNS

Due to the increasing adoption of public cloud services, virtualization, IoT, and emerging 5G technologies, enterprise network services and users, e.g., remote workforce, can be at any physical location. This results in that network perimeter cannot be defined precisely anymore, making adequate access control with traditional perimeter-based network security models (e.g., firewall, DMZ) challenging. The Zero Trust (ZT) network access framework breaks with this traditional approach by removing the implicit trust in the network. ZT demands strong authentication, authorization, and encryption techniques irrespective of the physical location of the devices. While several prominent companies have embraced ZT (e.g., Google, Microsoft, Cloudflare), its adoption has several obstacles.