Reviewerly/exHarmony huggingface dataset:
A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion

Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models.
Bridging the Gap between Purpose-Driven Frameworks and Artificial General Intelligence

Artificial General Intelligence (AGI) has leaped forward in capabilities, offering applications that reach far beyond conventional machine learning systems [...]
Nodes selection review for federated learning in the blockchain‐based internet of things

Abstract Internet of Things (IoT) gained momentum these last few years pushed by the emergence of fast and reliable communication networks such as 5G and beyond. IoT depends on collecting information from the environment, leading to a significant increase in the amount of data generated that needs to be transmitted, saved, and analyzed. It is clear that classical deterministic approaches might not be suitable to this complex and fast evolving environment. Hence, machine learning techniques with their ability to handle such a dynamic context, are rising in popularity. In particular, Federated Learning architectures which are better suited to the distributed nature of IoT and its privacy concerns. Besides, to address security risks such as model poisoning, device compromise, and network interception, Blockchain (BC) is seen as the secure and distributed underlying communication infrastructure of choice. This integration of IoT, FL, and BC remains in its early stages and several challenges arise. Indeed, nodes selection to perform resource intensive and critical operations like model learning and transactions validation is a crucial issue considering the strong heterogeneity of the involved devices in terms of resources. In this paper, we propose an original literature review including a taxonomy, a thorough analysis, a comparison of the proposed approaches, along with some open research directions.
Automatic Construction of Educational Knowledge Graphs: A Word Embedding-Based Approach

Knowledge graphs (KGs) are widely used in the education domain to offer learners a semantic representation of domain concepts from educational content and their relations, termed as educational knowledge graphs (EduKGs). Previous studies on EduKGs have incorporated concept extraction and weighting modules. However, these studies face limitations in terms of accuracy and performance. To address these challenges, this work aims to improve the concept extraction and weighting mechanisms by leveraging state-of-the-art word and sentence embedding techniques. Concretely, we enhance the SIFRank keyphrase extraction method by using SqueezeBERT and we propose a concept-weighting strategy based on SBERT. Furthermore, we conduct extensive experiments on different datasets, demonstrating significant improvements over several state-of-the-art keyphrase extraction and concept-weighting techniques.
High-Quality Object Detection Method for UAV Images Based on Improved DINO and Masked Image Modeling

The extensive application of unmanned aerial vehicle (UAV) technology has increased academic interest in object detection algorithms for UAV images. Nevertheless, these algorithms present issues such as low accuracy, inadequate stability, and insufficient pre-training model utilization. Therefore, a high-quality object detection method based on a performance-improved object detection baseline and pretraining algorithm is proposed. To fully extract global and local feature information, a hybrid backbone based on the combination of convolutional neural network (CNN) and vision transformer (ViT) is constructed using an excellent object detection method as the baseline network for feature extraction. This backbone is then combined with a more stable and generalizable optimizer to obtain high-quality object detection results. Because the domain gap between natural and UAV aerial photography scenes hinders the application of mainstream pre-training models to downstream UAV image object detection tasks, this study applies the masked image modeling (MIM) method to aerospace remote sensing datasets with a lower volume than mainstream natural scene datasets to produce a pre-training model for the proposed method and further improve UAV image object detection accuracy. Experimental results for two UAV imagery datasets show that the proposed method achieves better object detection performance compared to state-of-the-art (SOTA) methods with fewer pre-training datasets and parameters.
Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost

An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. We used two decision-tree classifiers to identify the diagnostic algorithms: RF and XGBoost. Bayesian optimization was used to optimize the hyperparameters of RF and XGBoost. Accuracy and area under the curve (AUC) were used as evaluation metrics. The XGBoost classifier outperformed the RF classifier with an accuracy of 81% and an AUC of 85%. A combination of symptom-physical signs and lung function tests was successfully used to construct a diagnostic algorithm on importance features for diagnosing adult asthma. These results indicate that the proposed model can be reliably used to construct diagnostic algorithms with selected features from objective tests in different settings.
A blockchain-based secured system using the Internet of Medical Things (IOMT) network for e-healthcare monitoring

The Internet of Things (IoT) has expanded because of the development of information technology. IoT impacts several fields, including the medical profession, smart cities, and data management. The development of sustainable medical systems is significantly aided by the Internet of Medical Things (IoMT). IoMT has a big impact on healthcare since it makes it easier to monitor and validate data about patients before storing it in the cloud. Keeping all data confidential and secured is essential since the IoMT is a big-data platform that is expanding quickly.This paper will apply this technical advancement to the realm of health, namely e-health. The research provides an IoMT-based monitoring tool for personal health surveillance. Block chain technology may resolve present interoperability issues in health-related systems and offer technological solutions that allow the electronic transfer of medical records to medical organizations and medical specialists. However, the transmission and security of people's health data remain a major concern in IoMT devices across a wide range of product categories. This research uses block chain as a secure solution to satisfy the requirements of medical privacy and data security. In this study, a Block chain-based IoMT security System (BC-IoMT-SS) is presented based on BC technology in IoMT for the security, privacy, and management of patient information. The suggested framework is successfully implemented to fulfill the optimal privacy and safety criteria for medical data management in IoMT gadgets. Utilizing the block chain's key, a healthcare application system has been created in which the person's health data may safely generate alerts for verified healthcare practitioners. Simulation results demonstrate that the suggested BC-IoMT-SS technique produces a high precision ratio of 94%, an efficiency ratio of 94%, a shorter delay of 0.63s, and a shorter reaction time compared to other current methods.
High-dimensional time-frequency entanglement in a singly-filtered biphoton frequency comb

Abstract High-dimensional quantum entanglement is a cornerstone for advanced technology enabling large-scale noise-tolerant quantum systems, fault-tolerant quantum computing, and distributed quantum networks. The recently developed biphoton frequency comb (BFC) provides a powerful platform for high-dimensional quantum information processing in its spectral and temporal quantum modes. Here we propose and generate a singly-filtered high-dimensional BFC via spontaneous parametric down-conversion by spectrally shaping only the signal photons with a Fabry-Pérot cavity. High-dimensional energy-time entanglement is verified through Franson-interference recurrences and temporal correlation with low-jitter detectors. Frequency- and temporal- entanglement of our singly-filtered BFC is then quantified by Schmidt mode decomposition. Subsequently, we distribute the high-dimensional singly-filtered BFC state over a 10 km fiber link with a post-distribution time-bin dimension lower bounded to be at least 168. Our demonstrations of high-dimensional entanglement and entanglement distribution show the singly-filtered quantum frequency comb’s capability for high-efficiency quantum information processing and high-capacity quantum networks.
A Fast-Compressive Tracking Integrated with Differential Evolution to Optimize Object Tracking Performance

It is difficult to develop a desired efficient tracking algorithm and model for object tracking because each tracker has its dominant attributes and weakness. This work chooses fast-compressive tracking for its rapid real-time tracking with the element of compressed sensing theory. In addition, an evolutionary computation method, differential evolution (DE), is introduced to search for the optimal parameters by iteratively solving and improving a solution with a proper objective function. Hence, the proposed fast-compressive tracking integrated with differential evolution (FCTDE) has improved the object tracking performance. Furthermore, the integration has resulted in a reduction in the computational complexity of the tracking procedure. The experimental results are recorded on the standard Online Tracking Benchmark (OTB100) data sets to represent the robustness of the proposed tracker. As a result, the proposed improvement algorithm's average overlapping ratio and center location error (CLE) improved to 0.51 and 28.87. Thus, the proposed model enhances the ability to tackle occlusion and out-of-plane rotation problems.
Adaptive Graph Embedding with Consistency and Specificity for Domain Adaptation

Domain adaptation (DA) aims to find a subspace, where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well. Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity (AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity (GECS), and adaptive graph embedding (AGE). GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS, the neighborhood samples with the same label are rewarded, while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.
FIDES: A Proposal for Federated Accountability in the Compute Continuum

In this visionary article, we present the concept of federated accountability, an innovative approach that distributes accountability-related computation and data across the compute continuum. To demonstrate the feasibility and versatility of our approach, we developed a prototype using blockchain technology that serves as a tangible illustration of how federated accountability can be applied across various domains.
All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential

The benefits of traceability have widely been discussed in research. However, studies have also shown that traceability practices are still hardly established in industrial settings due to the high manual effort and lack of tool support. In this paper, we explore the feasibility of using eye tracking to automatically detect trace links to reduce manual effort and thereby increase practical applicability. We conducted an interview study with 20 practitioners to give an overview of traceability practices in industry and how an eye tracking approach would need to be applied in order to be useful. As there is no one-fits-all solution to traceability, and technical limitations of eye tracking still exist, we collected information on used artifact types, tools and requirements management practices to adjust an approach to actual traceability stakeholders' needs. We report on perspectives from different roles in software projects and give an overview of traced artifacts, current traceability purposes and issues, as well as benefits and doubts concerned with using eye tracking to obtain links automatically. We discuss the implications for the implementation of an automatic tracing approach in practice and how eye tracking can support requirements engineering activities.
A Framework for Learning Behavior Trees in Collaborative Robotic Applications

In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.
Quantifying Requirements Technical Debt: A Systematic Mapping Study and a Conceptual Model

Requirements Technical Debt (RTD) is a research area where the Technical Debt (TD) metaphor is used to capture the consequences of sub-optimal decisions made concerning Requirements. Understanding the quantification of RTD is key to its management. To facilitate this understanding, we model the quantification of RTD. Our work is grounded in the literature found via a Systematic Mapping Study (SMS) and informed by prior work modeling the quantification of TD for software code-related TD types. This paper reports on the SMS and the development of our model, the RTD Quantification Model (RTDQM). The key observation from our work is that, although RTD is similar in most aspects to TD in software code, it also has its own components. Requirement artifacts have a feedback loop involving the User to precisely capture User Needs. RTD Interest (i.e., additional costs due to sub-optimal decisions concerning Requirements) can incur during both Requirements Engineering and Implementation activities. Furthermore, RTD can incur regardless of the presence of software code-related TD. Similar to benefits accrued by refactoring software code, rectifying RTD can also accrue benefits.
Sources of Opacity in Computer Systems: Towards a Comprehensive Taxonomy

Modern computer systems are ubiquitous in contemporary life yet many of them remain opaque. This poses significant challenges in domains where desiderata such as fairness or accountability are crucial. We suggest that the best strategy for achieving system transparency varies depending on the specific source of opacity prevalent in a given context. Synthesizing and extending existing discussions, we propose a taxonomy consisting of eight sources of opacity that fall into three main categories: architectural, analytical, and socio-technical. For each source, we provide initial suggestions as to how to address the resulting opacity in practice. The taxonomy provides a starting point for requirements engineers and other practitioners to understand contextually prevalent sources of opacity, and to select or develop appropriate strategies for overcoming them.
What Can be Concluded from User Feedback? - An Empirical Study

Crowd-based Requirements Engineering supports capturing high amounts of user feedback in order to understand what users think about a software and which changes they are interested in. While much progress has been made in automatically classifying the feedback, it is less clear which conclusions can be drawn from feedback. Studies on utilization of feedback in industry report that companies are afraid that user feedback might be biased, because little is known about the users. In this paper, we report a preliminary empirical study with 100 participants where we asked users to give feedback on an app and also describe their comprehension of this app as an approximation of their opinion about the app as whole. We compare feedback and comprehension description by looking at the frequencies of mentioned features. We find that the feedback of frequent users (46 out of 100) differs only a little from the overall feedback. We confirm that feedback is biased in that users individually and as a group know more about the app (mentioned in their comprehension) than they tell in their feedback. However, the feedback does represent which features the users find important in their comprehension.
Traceability Evaluation in Requirements Engineering According to Automotive SPICE

Context: Traceability evaluation is essential for automotive system success, which involves assessing an organisation's ability to track and trace the requirements from the initial conception of the requirement to the delivery of the final product or system. Objectives: This paper presents the development of a graphical user interface which supports the traceability evaluation in PTC Integrity, by displaying the dependency tree, to facilitate consistency and impact analysis in Automotive SPICE standards. Methods: Qualitative and quantitative experimental design and agile development methods were used to identify problems and potential solutions, and user requirements were gathered and analysed thematically. Results: Conceptual ideas for the visualisation and configuration were offered, and keeping the limitations in mind, the dependency tree tool was developed. The scope for future development was also provided. Conclusions: The developed tool is beneficial in automotive development by saving time in impact analysis and improving consistency.
RClassify: Combining NLP and ML to Classify Rules from Requirements Specifications Documents

Typically, business applications have complex and extensive functionality. Often, rules are scattered throughout the application documentation and code, making it challenging to modify them. Because business rules change more frequently, it is preferable to externalize the rules and move them outside the application. The requirements specification can serve as a valuable source for extracting and classifying rules that can further aid in assigning the rules to the appropriate teams based on their areas of expertise and externalizing the rules in the implementation. We introduce RClassify, which extracts and classifies rules from requirements specifications spread across several NL documents into eight different business rule classes. RClassify combines NLP and ML-based classification approaches to improve classification accuracy. We discuss the implementation of this approach in three real-world large-size and complex products, demonstrating its effectiveness, experience, and lessons learned. While the findings are presented in the specific context of three industry products, we believe that researchers, practitioners, and tool vendors will find the insights and experiences gained from this approach applicable in other contexts.