Reviewerly/exHarmony huggingface dataset:
TAQWA: Teaching Adolescents Quality Wadhu/Ablution Contactlessly using Deep Learning

This research presents a unique and innovative approach to teaching young children the proper steps of ablution (wazoo/wudu) by utilizing a non-invasive sensing system integrated with deep learning algorithms. However, most existing ablution detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. We conducted experiments with a group of youngsters to evaluate the system’s effectiveness, demonstrating its potential in fostering a deeper appreciation and comprehension of religious practices among young learners. This innovative privacy-preserving ablution system employs state-of-the-art UWB-radar technology with advanced Deep Learning (DL) techniques to effectively address the challenges mentioned above. The core focus of this system is to categorize the four fundamental ablution steps: Wash Face 3x, Wash Hand 3x, Wash Head 1x, and Wash Feet 3x. By transforming the collected data into spectrograms and harnessing the sophisticated DL models VGG16 and VGG19, the proposed system accurately detects these ablution steps, achieving an impressive maximum accuracy of 97.92% across all categories with the utilization of VGG16.
Smart Endotracheal Intubation: Real-Time Visual Feedback and Edge Detection for Enhanced Procedure Guidance

Endotracheal intubation (ETI) is a medical procedure that has gained researchers' interest in smart healthcare. Using endoscopy powered with Artificial Intelligence (AI) technology to detect anatomical features, making ETI smart is possible. However, the unavailability of public datasets and the insufficiency of predictive AI models for medical applications make this a challenging problem. This paper proposes a proof of concept powered by an ODROID XU4 microprocessor to provide visual feedback to the physician during the intubation procedure. The visual feedback is processed in real time using an edge detection technique, which assists the physician in visualizing the patient's vocal cords. The platform is capable of hosting deep learning models that utilize visual feedback and the detected edges to highlight anatomical features, and it is tested on a realistic manikin of a human airway.
Deep learning-based Human tracking, Face mask and Social distance monitoring, systems using YOLOv5

Wearing masks is legally mandated in numerous countries globally, and there is a growing trend where private entities in those regions are also adopting this practice. It can be challenging to monitor compliance with these essential social distance standards in large settings. While vaccines are available, social distance and wearing masks are considered appropriate rules besides the spread of the pandemic corona family infection in the current situation. Our paper's objective is to develop a deep learning framework that facilitates both social distance monitoring and face mask recognition systems. A self-making calibration video tests it from a camera. Now recognize the humans in video sequences using the YOLOv5 pre-train human tracking model, and in this model, social distance tracking with rectangle bounding boxes, and again, a new YOLOv5 bird's eye view approach used with circular bounding boxes. Our face mask action recognition model has achieved 97.5% mean average precision. Now results show that the recognized system effectively identifies individuals who violate public distances.
An Analysis of Blockchain Frameworks for Electronic Health Records Using Hyperledger

Blockchain technology stands as a transformative and groundbreaking innovation in the realm of information technology, offering solutions to numerous data-sharing challenges. Its application extends to various sectors, with healthcare being a notable beneficiary due to its capacity to enhance the security and privacy of Electronic Health Records (EHRs). This technology can empower patients with greater control over their data. In healthcare, safeguarding the security and privacy of medical data is paramount, given the existing concerns related to sharing and accessing records through conventional digital solutions. While cloud-based EHRs facilitate secure data sharing, they are susceptible to cyberattacks. Blockchain-based EHR platforms, on the other hand, hold promise in bolstering security and privacy. By adopting this approach, it becomes feasible to update and access patient data in real-time without compromising its confidentiality and security. The objective of this survey is to analyze the advantages of integrating Hyperledger into EHRs for secure healthcare data storage and retrieval. Permissioned blockchain systems are recognized for their capability to provide decentralized trust and enhance privacy. Hyperledger is a popular choice among permissioned blockchains. Hence, this research comprehensively analyzes related literature to explore the advantages of integrating Hyperledger into EHRs for secure healthcare data management.
Link Resolver Survival Skills: Using a Team-Based Approach to Diagnosing and Fixing Link Resolver Problems

As personnel at a digital library with no dedicated electronic resources librarian, we have found creative ways to collaborate on electronic resources management. We created a workflow in which student workers and staff throughout the library diagnose and fix problems with the library’s link resolver. We used this workflow to audit our link resolver using a random sample of citations to which the library should have had full-text access. We assessed linking from our discovery search, Google Scholar, and the EBSCO database platform. In this paper we describe our workflow and report results of the link resolver audit.
Scaling the Library Resources with Zscaler

More and more institutions and universities are implementing security measures to prevent wide-spread cyberattacks. When institutions initiate new security applications, the library is often overlooked. Libraries have complex ecosystems where multiple applications and systems converge with user data and online library resources. These ecosystems have various considerations including multi-factor authentication, proxies, multiple virtual private networks (VPNs), knowledgebases, and vendor inconsistencies. Duke University Health recently launched data security software called Zscaler, which prevented on-campus users from accessing the library e-resources. This presentation describes how Duke University Medical Center Library staff discovered the access issue and tested various workarounds to allow patrons to access library e-resources. The discussion covered how staff continue to work with Duke University Health IT security staff and university administration to find resolutions for the complex integration of security software with the library’s online resources.
Shiksha Mitra: An Assamese Language AI Chatbot Using Deep Learning

This research paper presents “Shiksha Mitra”, an artificial intelligence chatbot that answers user queries in Assamese for educational purposes. The chatbot uses Assamese Natural Language Processing (ANLP) and deep learning techniques to identify relevant sentences and provide responses. Unlike many organizations that use English chatbots, this research aims to develop a data-driven, retrieval-based, closed domain chatbot that can interact with users in Assamese. The chatbot is trained with corpus data encoded in UTF-8 format using a train function adapter. A feedforward neural network is used to find the best match from the corpus and generate a suitable answer.
Evaluating Agile Neural Educational System for Effective Resource Management

The major challenge of most basic schools is inadequate educational resources despite a conscious effort to constantly provide. This is a result of inaccurate data management leading to inappropriate predictions for effective planning. The actual efficiency of a system is determined by its ability to predict real-life data with speed and accuracy. In this work, the neural educational expert system (ES) is evaluated using mathematical models for predicting the availability of resources for the growing school-aged population using a criteria-based formative evaluation to know resource life and its effect on availability. This will help in the decision to add more resources by knowing when and how the resources should be added. Technical mathematical model generation through differential equations is used to fuse the factors affecting the availability of educational resources. The real-life data is used in prediction regarding the actual enrollment of learners and the availability of resources. The model is evaluated and critically analyzed to know the degree of accuracy and the steady state. The findings revealed that the resources decay and attrite at an exponential rate in the long run and the constant number of resources provided cannot cater for the rate of decay, resulting in inadequacy. A proposed algorithm for managing the resources is presented.
A Model-driven and Template-based Approach for Requirements Specification

Requirements specification and verification play an important role in the certification of safety-critical software (SCS). These activities are costly and error-prone because SCS exhibit a high number of requirements and most SCS manufacturers are still using natural language to specify these requirements. On one hand, natural language can introduce ambiguity and inconsistency. On the other hand, formal languages add an overhead to the requirements specification because of their complexity. Controlled Natural Languages (CNLs) fill these gaps by offering a middle-ground solution, although not yet well adopted by the industry. In this paper, we introduce an approach that combines CNLs and model-driven engineering (MDE) for requirements specification. The approach was proposed to support an industrial partner in the certification process of a SCS. Our approach uses templates and relies on two types of models: models that specify the templates, and a model of the domain of the system at hand. Using models of the templates enables to automate some requirements analysis tasks. Using a domain model allows the auto-completion and verification of requirements specified using the templates. We implemented the approach and validated it using three case studies and more than a thousand requirements. We observed that our approach and underlying templates are applicable across domains and that the templates yield requirements with better quality in terms of necessity, ambiguity, completeness, singularity, and verifiability.
Manual Abstraction in the Wild: A Multiple-Case Study on OSS Systems’ Class Diagrams and Implementations

Models are a useful tool for software design, analysis, and to support the onboarding of new maintainers. However, these benefits are often lost over time, as the system implementation evolves and the original models are not updated. Reverse engineering methods and tools could help to keep models and implementation code in sync; however, automatically reverse-engineered models are typically not abstract and contain extensive information that prevents understanding. Recent advances in AI-based content generation make it likely that we will soon see reverse engineering tools with support for human-grade abstraction. To inform the design and validation of such tools, we need a principled understanding of what manual abstraction is, a question that has received little attention in the literature so far.Towards this goal, in this paper, we present a multiple-case study of model-to-code differences, investigating five substantial open-source software projects retrieved via repository mining. To explore characteristics of model-to-code differences, we, all in all, manually matched 466 classes, 1352 attributes, and 2634 operations from source code to 338 model elements (classes, attributes, operations, and relationships). These mappings precisely capture the differences between a provided class diagram design and implementation codebase. Studying all differences in detail allowed us to derive a taxonomy of difference types and to provide a sorted list of cases corresponding to the identified types of differences. As we discuss, our contributions pave the way for improved reverse engineering methods and tools, new mapping rules for model-to-code consistency checks, and guidelines for avoiding over-abstraction and over-specification during design.
Variability-aware Neo4j for Analyzing a Graphical Model of a Software Product Line

Comprehensive analysis of a software product line (SPL) is expensive because the number of products to be analyzed is exponential in the number of the SPL’s features. To compensate, we analyze a model of the SPL rather than the source code, thereby reducing the size of the artifact under analysis. In this paper, we facilitate SPL analysis by lifting the Neo4j query engine to apply to an SPL model, so that a Neo4j query returns variability-aware results that cover all the SPL’s products. We used the lifted Neo4j to analyze five nontrivial SPLs (with respect to dataflows, control-flows, component interactions, etc.) and found that the overhead for returning variability-aware results for the full SPL, versus the results for just one product, ranges from 1.88% to 456%. In comparison to related work V-Soufflé (a lifted Datalog engine), lifted Neo4j is able to report complete path results whereas V-Soufflé reports only endpoints of paths. When both analyzers report the same results (e.g., endpoints of paths), lifted Neo4j is usually more efficient.
Uncertainty-aware consistency checking in industrial settings

In this work, we explore how we can assist engineers in managing, in a lightweight way, both consistency and design uncertainty during the creation and maintenance of models and other development artifacts. We propose annotating degrees of doubt to indicate design uncertainties on elements of development artifacts. To combine multiple opinions, we use the fusion operators of subjective logic. We show how these annotations can be used to identify, prioritize, and resolve uncertainty and inconsistency. To do so, we identify the types of design uncertainty and inconsistency to be addressed in two concrete industrial settings and show a prototype implementation of our approach to calculating the uncertainty and inconsistency in these cases. We show how making design uncertainty explicit could be used to tolerate inconsistencies with high uncertainty, prioritize inconsistencies with low associated uncertainty, and uncover previously hidden potential inconsistencies.
Automatic Security-Flaw Detection Replication and Comparison

Threat Modeling is an essential step in secure software system development. It is a manual, attacker-centric approach for identifying architecture-level security flaws during the planning phase of software systems. In the last years, academia presented two methods to automate threat detection that do not focus on a particular class of security flaws but offer general-purpose means to describe security flaws.This paper compares both approaches on an equal data foundation that was published with one of the approaches. Therefore, we specify a model-to-model transformation for converting between the approaches to allow this conceptual replication. Additionally, we provide security flaw patterns for the second approach that any user of the approach can use. We then replicate the detection with the second security flaw detection approach to compare both approaches. We focus our analysis on differences between automation-specific and approach-specific finding misclassifications on identifying whether some flaws are harder to find with an automated approach than others.We find that missed flaws usually stem from the imprecise definition of security flaws, while incorrectly identified flaws are approach-dependent. Despite that, both approaches perform similarly. The knowledge base, the transformation scripts and the evaluation script are publicly available to support the research community.
Automated Grading of Use Cases

Use cases (UCs) play a crucial role in software engineering courses, with students frequently using them in assignments, projects, and exams. However, as the number of students enrolling in Computer Science and Software Engineering programs continues to rise, manual grading of these models is becoming increasingly time-consuming. While automated grading tools for class diagrams exist, the automation of grading use case models has received limited attention.This paper proposes an approach for automatically grading use cases. To compare a student’s solution to the teacher’s solution our approach uses structural matching, syntactic and semantic word matching, natural language processing for sentence matching, and flattening of use case hierarchies. The grading algorithm is evaluated on three actual undergraduate and graduate assignments that involve modeling a Gas Station fill-up scenario, a Supermarket checkout scenario, as well as the interactions involved in playing the board game Elfenroads. The results show that with some tuning our automatically determined grades lie within 7% difference of the instructor’s manual grades.
gLTSdiff: A Generalized Framework for Structural Comparison of Software Behavior

State machine models, such as labeled transition systems and (extended) finite automata, can be structurally compared, for instance to find potential behavioral regressions in new software versions, to evaluate the accuracy of different model learning algorithms, and to fingerprint software for security applications. The state-of-the-art LTSDiff structural comparison algorithm has limited assumptions, making it broadly applicable. However, representation-specific information is not taken into account, requiring adaptations to prevent sub-optimal or even invalid results. We propose gLTSdiff, which generalizes and extends LTSDiff, allowing a wide range of state machine models to be compared, by recursively comparing the structure of state and transition labels. Additional challenges that we faced while applying LTSDiff in industrial practice are also addressed by gLTSdiff, as it rewrites undesired difference patterns, supports comparison of any number of input models, and allows for an effort/quality trade-off. gLTSdiff is implemented as an extensible open source library for structural model comparison. Using multiple large-scale industrial and open source case studies, we evaluate both its practical value and its various improvements.
Model-Driven Prompt Engineering

Generative artificial intelligence (AI) systems are capable of synthesizing complex content such as text, source code or images according to the instructions described in a natural language prompt. The quality of the output depends on crafting a suitable prompt. This has given rise to prompt engineering, the process of designing natural language prompts to best take advantage of the capabilities of generative AI systems.Through experimentation, the creative and research communities have created guidelines and strategies for creating good prompts. However, even for the same task, these best practices vary depending on the particular system receiving the prompt. Moreover, some systems offer additional features using a custom platform-specific syntax, e.g., assigning a degree of relevance to specific concepts within the prompt.In this paper, we propose applying model-driven engineering to support the prompt engineering process. Using a domain-specific language (DSL), we define platform-independent prompts that can later be adapted to provide good quality outputs in a target AI system. The DSL also facilitates managing prompts by providing mechanisms for prompt versioning and prompt chaining. Tool support is available thanks to a Langium-based Visual Studio Code plugin.
Ensembles of BERT Models for Ancient Chinese Processing

Ancient Chinese holds a significant place within the rich cultural heritage of China. To aid in the organization and analysis of ancient Chinese texts, various natural language processing tools and methods had been proposed. The advent of pre-trained language models (such as BERT) had further facilitated the automated processing of ancient Chinese. Although these models have demonstrated distinct advantages in processing ancient Chinese, there has been no effort to integrate them in order to further enhance their performance. Therefore, we employed various ensemble learning methods to integrate these single models, namely Chinese BERT, Chinese RoBERTa, SikuBERT and SikuRoBERTa. We then examined how well these models performed in sequence labeling tasks related to ancient Chinese, including word segmentation, Part-of-speech tagging, and named entity recognition. The experimental results indicated the ensemble model consisting of SikuBERT + SikuRoBERTa with the grid search method achieved the best performance. Furthermore, our analysis highlighted the significance of selecting appropriate base models and the quantity for ensemble models to performance.
Intelligent Question-Answering System for Famous Towns and Villages Based on Knowledge Graph

With the growing attention from the public towards the preservation of famous historical and cultural towns and villages, there is an increasing demand for both technical aspects of preservation management and the demonstrative aspect of showcasing historical and cultural resources. Currently, knowledge of famous towns and villages is mainly obtained through independent information retrieval, resulting in low query efficiency and difficulties in uncovering hidden knowledge within these sites. To address these issues, we develop an intelligent question-answering system based on a knowledge graph for Chinese famous historical and cultural towns and villages. The question-answering system can effectively respond to user queries regarding the historical evolution, cultural characteristics, and preservation methods of famous towns and villages, providing a more convenient approach to the protection and inheritance of these cultural heritage sites.
Anomaly Detection Via Variational Quantum Algorithm

Anomaly detection plays a crucial role in identifying rare and critical events, anomalies, or outliers that are not part of the normal data distribution. With the large volume of modern data and the widespread use of sensors and IoT devices, datasets have evolved into high-dimensional structures, posing significant challenges to anomaly detection tasks. In this work, we investigate to employ a hybrid quantum-classical algorithm to help handle such challenges. It is mainly based on Local Outlier Factor (LOF) algorithm to detect anomalies. However, we enhance distance estimation and k-nearest neighbor search via quantum algorithms. The proof-of-principle numerical results demonstrate the performance of the proposed method is competitive to previous algorithm.
A Joint Entity Relation Extraction Method Based on Type Prediction

Entity relation joint extraction is to extract entity and relation facts from unstructured text at the same time, which is a key step in constructing knowledge graph. Most of the existing joint extraction models adopt the method of extracting entities first and then classifying the relation. Although the performance is improved, the relation classifier is affected by redundant entities. Therefore, we propose a joint extraction model based on Relational Type Prediction (RTP). The type prediction attention mechanism is used to predict the relationship types of sentences, and a new sentence representation based on specific relationship types is obtained. Then each word in the sentence is mapped to the label space, the entity associated with the relation type is selected using softmax function, the entity location is marked using BIOES labeling strategy, and the triples are extracted. Experimental results on two public data sets show that this model can alleviate the problem of entity pair redundancy in overlapping entity relationship extraction, improve the interaction between entity relationships, and extract triples effectively.