Reviewerly/exHarmony huggingface dataset:
Towards Unveiling Effects Of Human Factors Within Security Risk Assessment

[Background:] Security-by-design techniques (e.g., STRIDE) are used to elicit system threats before they are exploited. Since security threat assessment is performed on a conceptualised model of the system under analysis, human expertise is relied upon to exhaustively elicit all possible threats. To this end, the outcomes of threat analysis depend on the individual actors involved in the process. However, human expertise can be biased depending on certain or a combination of human factors. [Goal:] With this work, we aim to unveil the effect (if any) of human factors (e.g., gender, age, seniority, educational background, nationality) to security risk assessment. [Method:] To contribute to this body of knowledge, we are conducting a state-of-the-art literature review and several experiments with human participants (experts and non-experts) in the domain of security and risk assessment. First, the topic and technical domain are described in general. Second, preliminary results of the on-going literature review are presented. Finally, a research plan is described including research questions, treatment, and participant recruitment.
Summary of the 1st Natural Language-based Software Engineering Workshop (NLBSE 2022)

Natural language processing (NLP) refers to automatic computa- tional processing of human language, including both algorithms that take human-produced text as input and algorithms that pro- duce natural-looking text as outputs. There is a widespread and growing usage of NLP approaches to optimize many aspects of the development process of software systems. In particular, since natural language artifacts are used and reused during the software development lifecycle, the availability of natural language-based approaches and tools enabled the envisioning of methods for im- proving efficiency in software engineers, processes, and products. The research community has been discussing these approaches in the 1st edition of the Natural Language-Based Software Engineer- ing Workshop (NLBSE), collocated with ICSE (the International Conference on Software Engineering) in 2022. This event brought together researchers and industrial practitioners from NLP and the software engineering community to share experiences, pro- vide directions for future research, and encourage the usage of NLP techniques and tools for addressing software engineering- speci c challenges. In this paper, we present a summary of the 1st edition of the workshop, which comprised ve full papers, four short/position papers, ve tool competition/demonstration pa- pers, one keynote (\Deep Learning &amp; Software Engineering: Past, Present and Future"by Denys Poshyvanyk), followed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2022.github.io/index.html
Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems

[Background] Selecting an appropriate task is challenging for contributors to Open Source Software (OSS), mainly for those who are contributing for the first time. Therefore, researchers and OSS projects have proposed various strategies to aid newcomers, including labeling tasks. [Aims] In this research, we investigate the automatic labeling of open issues strategy to help the contributors to pick a task to contribute. We label the issues with APIdomains- categories of APIs parsed from the source code used to solve the issues. We plan to add social network analysis metrics gathered from the issues conversations as new predictors. By identifying the skills, we claim the contributor candidates should pick a task more suitable to their skill. [Method] We are employing mixed methods. We qualitatively analyzed interview transcripts and the survey's open-ended questions to comprehend the strategies communities use to assist in onboarding contributors and contributors used to pick up an issue. We applied quantitative studies to analyze the relevance of the API-domain labels in a user experiment and compare the strategies' relative importance for diverse contributor roles. We also mined project and issue data from OSS repositories to build the ground truth and predictors able to infer the API-domain labels with comparable precision, recall, and F-measure with the state-of-art. We also plan to use a skill ontology to assist the matching process between contributors and tasks. By quantitatively analyzing the confidence level of the matching instances in ontologies describing contributors' skills and tasks, we might recommend issues for contribution. In addition, we will measure the effectiveness of the API-domain labels by evaluating the issues solving time and the rate among the labeled and unlabelled ones. [Results] So far, the results showed that organizing the issues?which includes assigning labels is seen as an essential strategy for diverse roles in OSS communities. The API-domain labels are relevant, mainly for experienced practitioners. The predicted labels have an average precision of 75.5%. [Conclusions] Labeling the issues with the API-domain labels indicates the skills involved in an issue. The labels represent possible libraries (aggregated into domains) used in the source code related to an issue. By investigating this research topic, we expect to assist the new contributors in finding a task, helping OSS communities to attract and retain more contributors.
Dynamic Capacity Service for Improving CXL Pooled Memory Efficiency

Compute Express Link (CXL) pooled memory is gaining attention from the industry as a viable memory disaggregation solution offering memory expansion and alleviating memory overprovisioning. One essential feature for the efficient use of the pooled memory is to dynamically allocate or release memory from the pool based on hosts’ demands. We refer to this feature dynamic capacity service (DCS). This article introduces one of the industry’s first DCS implementation for CXL pooled memory. We demonstrate fully functional DCS by implementing a field-programmable gate array-based CXL pooled memory prototype and full software stacks. Our experiment shows that DCS can substantially improve system memory utilization by dynamically allocating and releasing memory resources on demand. We also present the lessons learned from the DCS implementation.
Internal defect detection method based on dual-channel speckle interferometry

A novel dual-channel speckle interferometry based on a monochromatic camera is proposed to quantitatively characterize internal defects in materials. The sensor of the monochromatic camera is divided into two independent and simultaneous imaging channels by integrating dual-biprism and dichroic filters. The combination of electronic speckle pattern interferometry and digital shearography allows for the simultaneous measurement of out-of-plane displacement and slope. The [4+1] phase-shifting algorithm is employed to dynamically detect of the sequential phase. The effectiveness and accuracy of the proposed method are verified by testing a centre-loaded circular plate. Furthermore, the developed system performs non-destructive testing on a thin metallic plate with internal defects. The experimental results show that the proposed method can successfully localize and detect internal defects.
Semantic Label Representations with Lbl2Vec: A Similarity-Based Approach for Unsupervised Text Classification

In this paper, we evaluate the Lbl2Vec approach for unsupervised text document classification. Lbl2Vec requires only a small number of keywords describing the respective classes to create semantic label representations. For classification, Lbl2Vec uses cosine similarities between label and document representations, but no annotation information. We show that Lbl2Vec significantly outperforms common unsupervised text classification approaches and a widely used zero-shot text classification approach. Furthermore, we show that using more precise keywords can significantly improve the classification results of similarity-based text classification approaches.
Identifying In-Service Teachers' Perceptions of Developing 21st Century Skills Through Science Education Using TPACK-21 Framework

In this digital age, science teachers are continuously confronted with new emerging opportunities and challenges as a result of the rapid rate of discoveries in science and technology and information technology as well. Science educators, therefore, emphasize restructuring the education system to prepare 21st century learners to face complex challenges and prepare them to avail the opportunities at their fullest. This present study aimed to identify in-service science teachers' perceptions of developing 21st century skills through science education by employing an updated TPACK-21 framework. A mixed-methods study, using a survey-based questionnaire and focus group interviews with selected teachers, was undertaken to collect teachers' perspectives. The study revealed that when technology is used by teachers, it is often paired with specific content and pedagogically very different from what teachers are used to performing. The results highlight the need to use technology for innovation and to renovate contemporary teaching practices for 21st century learning.
On the relationship between source-code metrics and cognitive load: A systematic tertiary review

The difficulty of software development tasks depends on several factors including the characteristics of the underlying source-code. These characteristics can be captured and measured using source-code metrics, which, in turn, can provide indications about the difficulty of the source-code. From a cognitive perspective, this difficulty is due to an increase in developers' cognitive load, which can be estimated using psycho-physiological measures. Based on these measures, a handful of studies investigated the relationship between source-code metrics and cognitive load. For most of the metrics, such a relationship could not be established. While these studies used a small subset of metrics, the literature comprises hundreds of other metrics. Despite the existing reviews surveying these metrics, a consolidated overview is still needed to understand their properties and leverage their potential to align with cognitive load. This need is addressed in this paper through a Systematic Tertiary Review (STR) covering the full spectrum of source-code metrics, studying their properties and investigating their potential relationship to cognitive load. The outcome of this STR is intended to guide practitioners in choosing appropriate metrics, set the grounds for conceptualizing the relationship between source-code metrics and cognitive load and raise new research challenges for the future.
A tradeoff paradigm shift in cryptographically-secure pseudorandom number generation based on discrete logarithm

Discrete logarithmic pseudorandom number generators are a prevailing class of cryptographically-secure pseudorandom number generators (CSPRNGs). In generators of this type, the security parameter affects both security and performance. This adds to the design complexity via creating a critical tradeoff between security and performance. This research is an attempt at shifting the security-performance tradeoff paradigm in this realm. To this end, we propose a modification to Gennaro’s pseudorandom number generator via replacing word-wise arithmetic operations with bit-wise logical operations in trapdoor and hard-core functions. The security of our generator (like that of Gennaro’s) is based on the hardness of a special variant of the discrete logarithm problem. We establish an equivalence between the specific variant of the discrete logarithm problem with the standard problem. Moreover, we demonstrate that in the modified generator, performance will be almost independent of the security parameter as logical operations can be performed in register level without the interference of the Arithmetic-Logic Unit (ALU). This relaxes the security-performance tradeoff and allows designers to maneuver more flexibly in the tradeoff space. We implement and evaluate our proposed generator and prove its security. Our CSPRNG is deemed random by all randomness tests in NIST SP 800-22 suite.
An evolutionary intelligent data analysis in promoting smart community

Smart community construction is an integral part of smart city construction, and smart community management requires huge amounts of data as support. Currently, the data generated by some smart communities is scattered, and this data needs further analysis to realize value. This paper primarily studies data classification and parameter optimization. First, a novel K‐means clustering group support vector machines (SVM) method is proposed for data classification. For the parameter optimization problem of SVMs, evolutionary computation is used to seek the optimal solution through iterative evolution in a population composed of some feasible solutions. Then, the improved gray wolf optimization (iGWO) algorithm is used to optimize parameters and select features of SVM. Finally, to alleviate the situation that the minority samples are easily misjudged as noise samples due to the redundant features in the initial data, an oversampling method based on the iGWO and synthetic minority oversampling technique (SMOTE) is proposed, called iGWO–SMOTE–SVM. The experimental results demonstrate that the suggested approach on the six UCI datasets has acceptable accuracy, F1, and G‐Mean, which can well serve the construction of smart communities.
Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis

Artificial intelligence (AI) systems are becoming wiser, even surpassing human performances in some fields, such as image classification, chess, and Go. However, most high-performance AI systems, such as deep learning models, are black boxes (i.e., only system inputs and outputs are visible, but the internal mechanisms are unknown) and, thus, are notably challenging to understand. Thereby a system with better explainability is needed to help humans understand AI. This paper proposes a dual-track AI approach that uses reinforcement learning to supplement fine-grained deep learning-based sentiment classification. Through lifelong machine learning, the dual-track approach can gradually become wiser and realize high performance (while keeping outstanding explainability). The extensive experimental results show that the proposed dual-track approach can provide reasonable fine-grained sentiment analyses to product reviews and remarkably achieve a 133% promotion of the Macro-F1 score on the Twitter sentiment classification task and a 27.12% promotion of the Macro-F1 score on an Amazon iPhone 11 sentiment classification task, respectively.
Machine Learning Model for Offensive Speech Detection in Online Social Networks Slang Content

The majority of the world’s population (about 4 billion people) now uses social media such as Facebook, Twitter, Instagram, and others. Social media has evolved into a vital form of communication, allowing individuals to interact with each other and share their knowledge and experiences. On the other hand, social media can be a source of malevolent conduct. In fact, nasty and criminal activity, such as cyberbullying and threatening, has grown increasingly common on social media, particularly among those who use Arabic. Detecting such behavior, however, is a difficult endeavor since it involves natural language, particularly Arabic, which is grammatically and syntactically rich and fruitful. Furthermore, social network users frequently employ Arabic slang and fail to correct obvious grammatical norms, making automatic recognition of bullying difficult. Meanwhile, only a few research studies in Arabic have addressed this issue. The goal of this study is to develop a method for recognizing and detecting Arabic slang offensive speech in Online Social Networks (OSNs). As a result, we propose an effective strategy based on the combination of Artificial Intelligence and statistical approach due to the difficulty of setting linguistic or semantic rules for modeling Arabic slang due to the absence of grammatical rules. An experimental study comparing frequent machine learning tools shows that Random Forest (RF) outperforms others in terms of precision (90%), recall (90%), and f1-score (90%).
SGPT: Semantic graphs based pre-training for aspect-based sentiment analysis

Previous studies show effective of pre-trained language models for sentiment analysis. However, most of these studies ignore the importance of sentimental information for pre-trained models. Therefore, we fully investigate the sentimental information for pre-trained models and enhance pre-trained language models with semantic graphs for sentiment analysis. In particular, we introduce Semantic Graphs based Pre-training(SGPT) using semantic graphs to obtain synonym knowledge for aspect-sentiment pairs and similar aspect/sentiment terms. We then optimize the pre-trained language model with the semantic graphs. Empirical studies on several downstream tasks show that proposed model outperforms strong pre-trained baselines. The results also show the effectiveness of proposed semantic graphs for pre-trained model.
Handwritten Numeral Recognition Integrating Start–End Points Measure with Convolutional Neural Network

Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification accuracy of the similarly shaped handwritten numerals incorporating the terminals points with CNN’s recognition, which can be utilized in various emerging applications related to language translation. In handwritten numerals, the terminal points (i.e., the start and end positions) are considered additional properties to discriminate between similarly shaped numerals. Start–End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, the classification outcome of a CNN-based system is considered according to the highest probability exposed for a particular numeral category. In the proposed system, along with such classification, its probability value (i.e., CNN’s confidence level) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures the start-end points of the numeral image, suggesting the numeral category for which measured start-end points are found close to reference start-end points of the numeral class. Finally, the output label or system’s classification of the given numeral image is provided by comparing the confidence level with a predefined threshold value. SEWM-CNN is a suitable HNR method for Bengali and Devanagari numerals compared with other existing methods.
Unsupervised Handwritten Signature Verification with Extreme Learning Machines

Handwritten signature verification has two approaches based on online or offline data collection, both of them being supervised machine learning tasks. This work investigates the feasibility of unsupervised signature verification. It is inspired by a model-based forged signature generation approach, whose inversion could potentially provide an unsupervised solution for the signature verification task. The model inversion is attempted on a massive collection of image patches taken from samples of a large GPDSS10000 artificial signature verification dataset, pre-processed by a general-purpose deep learning network that extracts 1024 meaningful image features. An Extreme Learning Machine (ELM) solves the inversion problem at a very large scale. The paper proposes practical ways of ELM model structure selection on massive datasets and faster solvers. The results show the feasibility of an unsupervised solution for signature verification.
A Novel Methodology for Object Detection in Highly Cluttered Images

Over the past several decades camera technology has exploded in terms of higher quality, decreased cost, and increased availability. This means that there are significantly more images and videos being collected, which has developed an increased need for machine learning algorithms and methodologies that can extract and leverage relevant information from them. While much work has been done, current algorithms fall short on being able to both locate and classify objects in highly cluttered images, where it is difficult for even a human to identify objects. In this paper, we create and test a novel methodology to perform object detection in highly cluttered images by utilizing the partial least squares algorithm for dimensionality reduction, transfer learning for feature extraction, a newly developed object localization technique, and an ensemble of Extreme Learning Machines for classification. This methodology outperforms the current state of the art, Google’s AutoML Vision.
Extreme Learning Machines for Offline Forged Signature Identification

Signature verification has a crucial role in individual authentication process, and forged signatures can cause vast damages in all the fields. An important aspect for signature verification is that there are millions of credit card transactions providing signatures; therefore, speedy checks are required. We propose using extreme learning machines (ELM) to help with the task. We demonstrate the effectiveness and efficiency of using ELM detecting forged signatures. The proposed method reports 0.27% equal error rate, compared to recent 0.88% reported results. Linear neuron type outperforms other neuron types in this task, as it converges the fastest regardless of feature extractors used. However, the best performance was still reached by using RELU with SigNet as feature extractor.
k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

The recognition of hand signs is essential for several applications. Due to the variation of possible signals and the complexity of sensor-based systems for hand gesture recognition, a new artificial neural network algorithm providing high accuracy with a reduced architecture and automatic feature selection is needed. In this paper, a novel classification method based on an extreme learning machine (ELM), supported by an improved grasshopper optimization algorithm (GOA) as a core for a weight-pruning process, is proposed. The k-tournament grasshopper optimization algorithm was implemented to select and prune the ELM weights resulting in the proposed k-tournament grasshopper extreme learner (KTGEL) classifier. Myographic methods, such as force myography (FMG), deliver interesting signals that can build the basis for hand sign recognition. FMG was investigated to limit the number of sensors at suitable positions and provide adequate signal processing algorithms for perspective implementation in wearable embedded systems. Based on the proposed KTGEL, the number of sensors and the effect of the number of subjects was investigated in the first stage. It was shown that by increasing the number of subjects participating in the data collection, eight was the minimal number of sensors needed to result in acceptable sign recognition performance. Moreover, implemented with 3000 hidden nodes, after the feature selection wrapper, the ELM had both a microaverage precision and a microaverage sensitivity of 97% for the recognition of a set of gestures, including a middle ambiguity level. The KTGEL reduced the hidden nodes to only 1000, reaching the same total sensitivity with a reduced total precision of only 1% without needing an additional feature selection method.