Reviewerly/exHarmony huggingface dataset:
Possibilistic Cost Computation Tree Logic and Related Equivalence, Abstraction Technique

Recently, probabilistic Kripke structures have been used to represent uncertain systems; nevertheless, important transition costs were ignored in earlier studies, making it impossible to model some uncertain systems with costs.To compensate for the major shortcoming, this research addresses the transition costs suggested in uncertain systems.First of all, we offer possibilistic Kripke cost structures and associated possibilistic costs such as cumulative cost and possibilistic expected cost.As the verification model, furthermore, we investigated the syntax and semantics of possibilistic cost computation tree logic and utilized them to develop verification characteristics of possibilistic Kripke cost structures.Finally, as solutions to the state space explosion problem of possibilistic cost computation tree logic model checking, abstraction and equivalence are presented as methods to reduce the state numbers and frameworks of possibilistic Kripke cost structures.
The design of a S-box based on DNA computing and chaos theories

Due to the continuous growth of systems operating on big data, the scope of cloud computing, and the applications of Internet of Things, data security becomes increasingly important. Substitution(S)-box is a vital non-linear component which challenges the design and implementation of cryptographic systems. This chapter presents a new method to design S-boxes synergizing deoxyribonucleic acid (DNA) computing and chaos theories. A 128-bit secret key is transformed to generate the initial conditions and parameters used in a hyperchaotic system with four-wing attractors to generate chaotic sequences. Subsequently, DNA complementary rules are randomly selected to encode the chaotic sequences to obtain three DNA codes, which dedrive two independent systems of piecewise linear chaotic maps (PWLCM) in order to generate required S-boxes. Numerical experiments for 1000 randomly generated S-boxes demonstrate that the results possess good bijection, ideal nonlinearity, strict avalanche criterion (SAC), valid bit independence criterion (BIC), differential approximation probability, and cryptographic key sensitivity. The proposed methodology for S-box generation provides better cryptographic properties and can be widely used in different security scenarios that overplay traditional technologies.
EduSense-AR: A Sensory Learning Solution for Autistic Children

Innovative teaching-learning techniques, in combination with computer assistive devices, can be useful solutions for children with autism and mild developmental delays. These devices augment the learning content through audio and video. Augmented Reality (AR) technology has been quite useful in creating such teaching-learning aids for autistic children. This paper proposes EduSense-AR, an AR application that can be used to familiarize autistic children with everyday elements of nature in real-time. The application can be run on any basic android device. The virtual 3D graphics with embedded audio call for the receptor sense organs of the children and facilitate their recognition and learning of the taught content. This app assists the autistic children to learn seasons with learning material presented on screen with the help of AR markers. The paper also discusses parents' opinions about the usefulness and acceptance of the application.
Fine-grained Early Frequency Attention for Deep Speaker Recognition

Attention mechanisms have emerged as important tools that boost the performance of deep models by allowing them to focus on key parts of learned embeddings. However, current attention mechanisms used in speaker recognition tasks fail to consider fine-grained information items such as frequency bins in input spectral representations used by the deep networks. To address this issue, we propose the novel Fine-grained Early Frequency Attention (FEFA) for speaker recognition in-the-wild. Once integrated into a deep neural network, our proposed mechanism works by obtaining queries from early layers of the network and generating learnable weights to attend to information items as small as the frequency bins in the input spectral representations. To evaluate the performance of FEFA, we use several well-known deep models as backbone networks and integrate our attention module in their pipelines. The overall performance of these networks (with and without FEFA) are evaluated on the VoxCeleb1 dataset, where we observe considerable improvements when FEFA is used.
Towards Blockchain-Enabled Intrusion Detection for Vehicular Navigation Map System

In recent years, with the fast development of autonomous driving technology, the Internet of Vehicles (IoV) has received much more attention regarding the security aspect. The traditional security defense strategies are not enough to cope with the risks faced by the IoV. The mature V2X communication protocols such as C-V2X and DSRC are not widely deployed because they have not reached consensus among telecom operators, vehicle manufacturers, and even governments. In the literature, some researchers proposed to combine blockchain technology, especially consortium blockchain, with IoV. However, we find that the consortium blockchain solution may also face some challenges that once an attacker gains control of permissioned nodes, they can upload fake or false data (e.g., forged geographic information and road information) to affect the system performance and security. Motivated by this issue, this paper focuses on vehicular navigation map and develops a blockchain-enabled intrusion detection scheme for vehicular navigation map systems. It can detect malicious nodes that exist in the blockchain in order to ensure the integrity of navigation map data when uploading.
A conceptual design of tailless minicopter

This paper describes the design process of a tailless mini copter having surveillance capability by integrating it with Internet of Things. The microprocessor controls different sensors of the tailless mini copter, where python programming language acts as an interface for the raspberry pi microprocessor. The major challenge is to overcome is the inertial rotation of the copter due to the absence of a tail rotor. The inertial rotation can be cancelled out by implementing the co-axial mechanism with contra-rotating propellers. The copter would make its place where other UAVs cannot reach, because of its compact design and low self-weight. Integrating more sensors would open a wide range of applications in different fields.
The Proximity Compatibility Principle and Its Applications to Human Perception of Graphic Displays

When engineering graphs, it is important that the engineer consider what display format will lead to both the most accurate and most rapid processing of information. The proximity compatibility principle (PCP) states that graphs with high display proximity (ex: line graphs in which lines connect data points) are better for tasks where participants must mentally integrate data points and predict trends, and those with low display proximity (ex: bar graphs, whose data points are unconnected) should be better for tasks that require focused attention on one specific data point. Participants in this experiment viewed either a bar or line graph and answered a corresponding true/false question (requiring either focused attention on a data point or integration across the points). ANOVA results support the PCP, finding a significant interaction between graph and question type for the accuracy of graph interpretation. A follow up experiment examining performance on tasks that required a hybrid of integration and focused attention processing revealed that those questions emphasizing trend integration were better supported by line graphs, whereas bar graphs better supported categorical integration.
Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning

Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model, in particular how well it matches a domain expert’s understanding of a task. Underspecification [6] refers to the existence of multiple models that are indistinguishable in their in-domain accuracy, even though they differ in other desirable properties such as out-of-distribution (OOD) performance. Identifying these situations is critical for assessing the reliability of ML models. We formalize the concept of underspecification and propose a method to identify and partially address it. We train multiple models with an independence constraint that forces them to implement different functions. They discover predictive features that are otherwise ignored by standard empirical risk minimization (ERM), which we then distill into a global model with superior OOD performance. Importantly, we constrain the models to align with the data manifold to ensure that they discover meaningful features. We demonstrate the method on multiple datasets in computer vision (collages, WILDS-Camelyon17, GQA) and discuss general implications of underspecification. Most notably, in-domain performance cannot serve for OOD model selection without additional assumptions (See https://arxiv.org/abs/2207.02598 for the full-length version of this work).
Towards the Analysis and Completion of Syntactic Structure Ellipsis for Inline Comments

The ellipsis of the syntactic structure is a common phenomenon in ordinary textual documents. Existing studies have found that despite syntactic ellipsis could help avoid repetition of normative documents, it could also, for example, lead to ambiguity and hamper the understandability of document contents. As a fundamental component of software, code comments are generally written by developers in a non-structured way just like normative documents. This naturally inspires us to explore whether syntactic ellipsis is also a common phenomenon in code comments and what potential negative effects would such ellipsis have on software tasks such as code/comments comprehension activities. Such explorations, in our opinion, are expected to facilitate the research on code comments and comments-related software tasks. To this end, we conduct the first large-scale study to explore the syntactic structure ellipsis problem of code comments, with a focus on Java inline comments. Specifically, we construct a data set of 1,000 Java projects with 1,307,457 inline comments and associated codes. Based on this data set, we first study the prevalence of syntactic structure ellipsis in inline comments. We find that syntactic structure ellipsis is quite common in inline comments where 83.6% comments have structure ellipsis (such as subject/predicate omissions). Then, we investigate the effects of syntactic structure ellipsis on code/comment understanding activities. As a result, we find that there indeed exists a negative relationship between them, with a medium effect size. Based on these findings, we further propose neural network based approaches to complete the ellipsis parts for the inline comments. With our approach, we could achieve: 1) a medium improvement in assisting code/comment understanding activities, and 2) a substantial improvement of 11.3% in comment-assisted code abbreviation extension task.
Anomaly Detection for the Industrial Internet of Things: an Unsupervised Approach for Fast Root Cause Analysis

The Industrial Internet of Things is one of the application area where Machine Learning has been most successful recently. In such context, typically Machine Learning-based solutions are consumed by users in the context of Decision Support Systems. Unfortunately, a major drawback of many Machine Learning approaches is that they calculate predictions but provide no explanations, which represent a major limitation in driving decision making: users may find predictions hard to understand and trust. Ultimately, this may prevent them from adopting Machine Learning-powered tools and turning their predictions into actionable insight. In this work, we propose an unsupervised anomaly detection system able to detect anomalies and explain predictions, thus enabling root cause analysis. The deployment of Industrial Internet of Things monitoring solutions through Decision Support Systems requires efficiency and very low latency. To achieve the goal, we combine Isolation Forest with a fast, model-agnostic interpretability approach, called Accelerated-agnostic Explanations (AcME). We evaluate the efficacy of the proposed approach in two real industrial cases.
An Autonomous Quadrotor Exploration Combining Frontier and Sampling for Environments with Narrow Entrances

An autonomous quadrotor exploration method based on frontier and motion primitives sampling is presented for exploring spatial structural environments with multiple narrow entrances, which is a rigorous challenge for traditional RRT methods. The exploration planning framework includes exploration target selection and autonomous navigation. The local region exploration is implemented by expanding the motion primitives sampling in the exploration target selection, while the global exploration is completed by searching the frontier viewpoints. For local exploration (LE), the motion primitives are expanded according to the quadrotor state, and the best primitive viewpoint is selected by calculating the score of the motion primitives. In global exploration (GE) process, the best global frontier viewpoint is selected, and the key path points are determined in a global undirected graph. After exploring target point is selected, the autonomous navigation module generates a safe, smooth, and dynamically feasible trajectory and enables the quadrotor follow it. The effectiveness and practicality of the exploration framework is validated through experimental simulations, and compared with other existing methods.
A Multi-Granularity Scene Segmentation Network for Human-Robot Collaboration Environment Perception

Human-robot collaboration (HRC) has been considered as a promising paradigm towards futuristic human-centric smart manufacturing, to meet the thriving needs of mass personalization. In this context, existing robotic systems normally adopt a single-granularity semantic segmentation scheme for environment perception, which lacks the flexibility to be implemented to various HRC situations. To fill the gap, this study proposes a multi-granularity scene segmentation network. Inspired by some recent network designs, we construct an encoder network with two ConvNext-T backbones for RGB and depth respectively, and an decoder network consisting of multi-scale supervision and multi-granularity segmentation branches. The proposed model is demonstrated in a human-robot collaborative battery disassembly scenario and further evaluated in comparison with state-of-the-art RGB-D semantic segmentation methods on the NYU-Depth V2 dataset.
Kinematically-consistent Real-time 3D Human Body Estimation for Physical and Social HRI

We present a software tool, fully integrated with ROS, that enables robots to perceive people full body in 3D. The system works either with a simple RGB camera, or a RGB-D camera for better 3D absolute position estimation. The system is based on Google Mediapipe, and runs at > 8Hz on CPU. The consistency of the human kinematic model is ensured by relying on a URDF-defined kinematic model, that could be adjusted to each person's anthropometric characteristics.
A multi-view extractive text summarization approach for long scientific articles

Automatic text summarization aims to generate a simple and descriptive summary covering the main content of a document. Most of the literature in automatic text summarization focuses on proposing and improving Deep Learning methods in order to make these models applicable in the context of long text summarization. Unfortunately, these models still have limitations on the input sequence length. Such a limitation may lead to a loss of information that impairs the quality of the summaries generated. For this reason, we propose a Multi-View Extractive Text Summarization approach for long scientific texts. Our hypothesis is that focusing on getting a better representation of the text is a key attribute for this task. We treat extractive summarization as a binary classification problem. We propose and evaluate a view fusion method that are independent of the classification model. We show that using this strategy we generate more consistent models that have a small score variance. Our experiments with real world articles show that our approach outperforms recent state-of-the-art models in extractive. abstractive and hybrid summarization.
Disciplinary E-Tutoring Based on the Domain Ontology ONTO-TDM

Due to the development of the web and the emergence of new technologies, teaching and training methods continue to evolve, providing then a new way to learn. But this is not without its drawbacks. These new methods of learning, known as online or distance learning, sometimes cause learners to “isolate”, which can lead to abandonment and failure in their tuition process. To overcome this problem and provide additional help for both tutors and learners, a question answering system based on a domain ontology, called ONTO-TDM (Ontology for teaching domain modeling), for distance tutoring assistance is described in this paper. The tool is based on the principles of natural language processing technologies (NLP), similarity analysis, and a knowledge base specific to the learning domain, helping to better understand and contextualize the learner's request to confer adequate responses, thereby reducing the sense of isolation.
Efficient Named Entity Recognition on Greek Legislation

The legal landscape is constantly becoming more and more complex. Artificial Intelligence and Machine Learning are starting to shine in many fields, so it is only natural that we examine their implementation in the Legal Domain as well. While many researchers are investigating the application of these techniques in English, for low-resource languages like Greek, the field is far from explored. Our work is a step in that direction. We describe our Named Entity Recognition solution to Greek Legislation and compare it with the available state-of-the-art Greek implementations and present how our approach outperforms them.
Rough–Granular Approach in Imbalanced Bankruptcy Data Analysis

During the global economic crisis data were collected on the financial condition of Polish enterprises. Correct analysis of this type of data gives a chance to draw conclusions and avoid the problem of bankruptcy in the future. Unfortunately, the collected data have imbalanced characteristics. Hence, they require a dedicated processing strategy. In this paper, we propose to use an optimized version of our rough–granular approach (RGA) to improve the classification efficiency of bankruptcy data. The RGA oversampling algorithm generates new minority class samples in specific areas of the feature space taking into consideration additional difficulty factors. Moreover, the method in the filtering step provides removal of generated ambiguities. This approach has been successful with bankruptcy data. The study showed in most cases an improvement in the detection rate of the minority class compared to the classification without preprocessing and the SMOTE technique. The RGA algorithm was confirmed to be effective in terms of AUC and recall measures on imbalanced data with complex distribution. The accuracy metric proved to be unreliable for data with these characteristics. XGBoost was used as classifier. In our experiments, we focused on an in-depth analysis of the types of information granules present in the dataset and their impact on classification performance.
Improved VGG Architecture in CNNs for Image Classification

Apart from computer vision, deep learning has brought the concept to a new era of machine learning. One of the deep learning approaches for classification analysis is Convolutional Neural Networks (CNNs), a model of artificial neural network that has often been the most popular approach in computer vision. In recent decades, many approaches for image classification have been proposed. To obtain high accuracy, most studies focused on deepening and enlarging the CNNs architecture such as the VGG network. However, deep and complex architecture, on the other hand, can result in extraordinarily long execution time. This study primarily aims to classify images using the improved VGG architecture to minimize the execution time and enhance the classification performance. The comparative experiments of the proposed architecture with another three existing architectures have been made and trained with six different datasets from Kaggle. As a result, the execution time and the classification accuracy of the proposed architecture is better than the other three existing architecture. Hence, the proposed architecture indicates that the execution time and the classification performance can be improved by downsized the VGG architecture.
Secure Development Workflows in CI/CD Pipelines

Modern development workflows heavily utilize continuous integration (CI) and continuous delivery (CD) pipelines. CI/CD pipelines run with highly privileged credentials capable of accessing code repositories, writing to artifact registries, and in many cases deploying software into production. This represents an attractive target for adversaries. As such, the security of these pipelines and associated workflows is as critical as the security of the developed code. Secure development therefore encompasses writing secure code and securing the workflows for integration and deployment. In this paper, we present the key areas demanding attention when designing secure development workflows and associated CI/CD pipelines.
AMIGUS: A Robot Companion for Students

The pandemic has made everybody stay studying at home, but also there have always been isolated people who need a little push to study or interact with others. AMIGUS is a social robot that helps with good companionship, motivational messages and tasks during the times when students are dealing with online classes.
Spike – A code editor plugin highlighting fine-grained changes

Information about source code changes is important for many software development activities. As such, modern IDEs, including, IntelliJ IDEA and Visual Studio Code, show visual clues within the code editor that highlight lines that have been changed since the last synchronization with the code repository. However, the granularity of the change information is limited to a line level, showing mainly a small colored icon on the left side of the lines that have been added, deleted, or modified.This paper introduces Spike, a source code highlighting plugin that uses the font color to visually encode fine-grained version difference information within the code editor. In contrast to previously mentioned tools, Spike can highlight insertions, deletions, updates, and refactorings all in a same line. Our plugin also enriches the source code with small icons that allow retrieving detailed information about a given code change. We perform an exploratory user study with five professional software engineers. Our results show that our approach is able to assist practitioners with complex comprehension tasks about software history within the code editor.