Reviewerly/exHarmony huggingface dataset:
Combined Aerial Cooperative Tethered Carrying and Path Planning for Quadrotors in Confined Environments

In this article, a novel aerial cooperative tethered carrying, and path planning framework is introduced with a special focus on applications in confined environments. The proposed work is aiming towards solving the path planning problem for the formation of two quadrotors, having a rope hanging below them and passing through or around obstacles. A novel composition mechanism is proposed, which simplifies the degrees of freedom of the combined aerial system and expresses the corresponding states in a compact form. Given the state of the composition, a dynamic body is generated that encapsulates the quadrotors-rope system and makes the procedure of collision checking between the system and the environment more efficient. By utilizing the above two abstractions, an RRT path planning scheme is implemented and a collision-free path for the formation is generated. This path is decomposed back to the quadrotors’ desired positions that are fed to the Model Predictive Controller (MPC) for each one. The efficiency of the proposed framework is experimentally evaluated.
Spline-Based Dynamic Object Handling in Autonomous Vehicles: A Model-Based Path Planning Algorithm

In this study we propose a model-based dynamic path planning algorithm that is designed to navigate Autonomous Vehicles through complex and dynamic environments. To achieve that, a novel spline-based approach is utilized for the production of several candidate paths along a predetermined route and a Gaussian-based function is utilized for their evaluation. Our algorithm takes into account various factors, such as static and dynamic objects, to make the appropriate decisions for the vehicle’s path, making it a promising solution for such objects during an autonomous vehicle navigation. The algorithm was tested in high-fidelity scenarios using CARLA Simulator, which is a powerful tool for simulating autonomous vehicle scenarios. The results indicate that the proposed algorithm is capable of generating efficient and safe paths for the vehicle to follow.
Hyperspectral fruit image restoration using non-convex optimization

Hyperspectral (HS) imaging captured at low proximity ranges has become an effective method for assessing the quality and safety of food products in recent years. The majority of Hyperspectral cameras are push-broom type, which often contributes random noise and stripes to the spectral images. As a result, the quality of the collected HS images and the precision of the subsequent processing will both suffer. Despite the fact that there are several denoising techniques that have been studied and recorded in the literature, they frequently fail to fully restore the original HS images when there are strong stripe noises and other intricate mixed noises. In this study, we introduce a new method based on tensor logarithmic Schatten-p norm minimization with anisotropic spatial spectral total variation (ASSTV) regularization for simultaneous denoising and destriping of line scan-based hyperspectral images. Tensor logarithmic Schatten-p norm improves denoising performance by deleting the smaller singular values while protecting the larger singular values. Through the application of ASSTV, stripes and Gaussian noise are eliminated while maintaining the spatial–spectral smoothness of HS images and the directional characteristics of the stripes. The augmented Lagrange multiplier (ALM) approach is used to address the ensuing optimization problem. The quantitative and visual assessments reveal that the suggested technique has improved performance than its counterparts.
Time-Aware Data Profiling With Decision Tree Pattern Mining

Data quality issues can lead to significant financial losses, which necessitates the use of data profiling to monitor the statistical properties of datasets and detect harmful deviations early. However, many existing data profiling solutions cannot perform multi-column feature analysis. On the other hand, machine learning algorithms, particularly decision trees, are effective at discovering non-linear and multivariate patterns within tabular data. In this study, we propose a framework that combines the interpretable pattern-mining capabilities of decision trees with time series forecasting to identify significant changes in data. We evaluate our framework on a real-world dataset from a leading telecommunications provider in Germany, which includes a known anomaly resulting from a faulty database entry. Our results indicate that our framework successfully detects data changes and provides interpretable descriptions for each anomaly, highlighting its relevancy for practitioners.
Towards a contextual model of software engineering capabilities: Factors affecting team performance

A prevalent research stream in the information systems domain is founded on the rationale that organizations need to establish and manage specific IT capabilities to achieve competitive advantage.More recently, research efforts have shifted from investigating organizational-level capabilities towards identifying team capabilities in order to support modern approaches to software engineering.The research at hand aims to identify contextual factors which influence these software engineering capabilities required by teams to achieve superior performance.To this end, we adopt a mixed-methods approach conducting a tertiary study and an empirical research component involving six domain experts.The tertiary study results in a taxonomy of 56 contextual factors which are then reduced and validated with regards to their relevance to software engineering teams through a Q-Sort ranking exercise and a subsequent focus group session.This results in a subset of 15 items which together form a contextual model of software engineering team capabilities along the dimensions technology, organization and environment.The paper discusses the mature research stream on information systems context in the new light of modern software engineering approaches.The resulting contextual model is of value to researchers and practitioners when analyzing their team context and deciding in which capabilities to invest.
English grammar multiple-choice question generation using Text-to-Text Transfer Transformer

English grammar multiple-choice questions (MCQs) can be automatically generated to reduce preparation time. Previous studies have focused on semiautomated methods based on the transformation of human-made sentences/articles into MCQs, owing to which the number of generated questions is dependent on the size of a given text corpus. This study proposes an artificial intelligence-assisted MCQ generation system that increases the number of generable questions using controllable text generation techniques. In this system, the questions for MCQs are generated using a text generation model trained using the Text-to-Text Transfer Transformer (T5) architecture, a powerful deep learning model for performing text generation tasks, with a keyword and a part-of-speech (POS) template as the input for content and grammar topic control. For the text-to-MCQ transformation process, answer-to-MCQ and distractor-selection strategies are proposed for 10 grammar topics using rule-based algorithms. The quality of the generated MCQs is evaluated by human experts. The acceptance rate of the questions generated using the proposed system is 86%. The controllability of content and grammar topics are 96.86% and 98.57%, respectively. The findings of this study show that the T5 model achieves good performance in terms of controlling the POS structure in a keyword-to-text generation task. Moreover, the good acceptance rate indicates that artificial intelligence has the potential to help teachers speed up the process of selecting examination questions. We also discuss extending the proposed system to other grammar topics and the limitations of the proposed system.
Supporting IoT system design for the location, use, and management of objects in health centers

Quickly locating objects in health centers is crucial for medical workers to provide their services properly. For instance, promptly locating certain medical equipment, such as wheelchairs and mobile beds, may be vital to saving hospital patients. Despite this, searching for medical equipment is frequently a manual procedure, which impacts the condition of patients and the workload of medical staff. This research contributes to digital transformation in health centers by improving medical work with the help of the Internet of Things, context awareness, and situation awareness. We propose a model to support the design of interactive software systems to locate objects to be used in health centers while managing their conditions in such a way that it will be possible to predict future events. Interviews were performed with a nurse, a radiologist, two pharmacists, and a biomedical engineer to understand better the use and management of medical equipment inside hospitals. The responses were compiled, analyzed, and applied to refine the model while demonstrating the feasibility of representing the situation awareness. A case study illustrates a common application where medical equipment is needed inside a hospital.
Machine Learning Approaches for Early Diagnosis of Breast Cancer: A Comparative Study of Performance Evaluation

Breast cancer is a leading cause of death among women worldwide. Early detection and diagnosis are crucial to improving the chances of survival. This paper presents a study on the diagnosis of breast cancer using various machine-learning approaches. The study includes the performance evaluation of nine different techniques using confusion matrix accuracy for Sensitivity, Specificity, Precision, PME, PPV, NPV, and Model Accuracy. AdaBoost is found to have the highest Sensitivity and PME, while Random Forest and MLP gave the best Specificity and Precision. Logistic Regression is found to be the best model for accuracy with 97.8%, followed by SVM with 96.49%, Random Forest with 95.61%, and KNN & Decision Forest with 94.73%. The proposed approach is found to have the highest accuracy of 97.80 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> compared to other approaches studied. Our results indicate that the proposed approach using discretization can significantly improve the signal-to-noise ratio in the diagnosis of breast cancer. This approach can accurately predict and diagnose breast cancer using a subset of features.
Traditional vs. low-code development: comparing needed effort and system complexity in the NexusBRaNT experiment

Low-code platforms have gained attention in recent years as a visual and model-driven approach to application development. This approach uses drag-and-drop elements, model-driven logic, and graphical user interfaces to democratize development, making it possible for developers or business analysts of various skill levels to produce applications. In this experiment, we compare the traditional development approach with a low-code one using a platform that executes models in DEMO language, as an information and workflow system. We make this comparison with NexusBRaNT as its object, a system projected to manage information of a research project, which targets health professionals who perform cognitive rehabilitation. Our findings show that, in this particular case, the low-code approach could deliver a high-quality and functional application with an impressive reduction in needed time and implementation complexity. Namely, the standard approach took 888 hours of work, while using low-code, just 47.5 hours of work were needed, a 94.63% reduction in the needed effort. Regarding complexity, the traditional approach led to a total of 12310 lines of code implementing the system, while the low-code approach led to a total of 1723 database records, a reduction of 86%. This is highly relevant, especially for organizations that need to rapidly develop and deploy applications. The results of this experiment have important implications for the future of software development, particularly in the context of digital transformation and the increasing demand for agile and rapid application development.
Towards a New Insider Threat Mitigation Framework

Insider threat agents have become a major concern for any organization. This research establishes the limitations of the traditional MOC (Motivation, Opportunity, and Capability) theory relied upon in cybersecurity to mitigate insider threats. Such limitations are supported by the ever-increasing sophistication and complexity of the characterization of insider threat landscape. We thus relied on studies undertaken in fraud theory to demonstrate that an element of Rationalization can be augmented to the MOC theory without loss of generality, paving the way therefore for the conceptualization of a new Insider Threat Mitigation Framework of which Rationalization becomes a significant determinant. This leads to a new cybersecurity theory, the MOCR theory. Our proposed MOCR theory states that a privileged user becomes a threat to an organization only when the four attributes are realized simultaneously to a variety of degrees by an insider. As such, the appropriate mitigation measure from an organization perspective is to dynamically ensure that, an insider keep their risk profile (MOCR profile) very low, at any given time, throughout their lifecycle in the organization. A fifth component, Influence (I) which has the effect of catalysing the attainment of MOCR was identified and its impact in attaining MOCR was discussed.
Verification of a Predictive Method for Obstacle Detection and Safe Operation of Autonomous Vehicles

This paper focuses on the verification of an object detection and avoidance method for autonomous vehicles to maneuver their way safely and efficiently. The model predictive control approach is explored and compared with other control approaches based on variables such as safe detection distance and safe operating time. The results indicate that the proposed approach could be promising for autonomous vehicles that often comprise various types of sensors and components and could otherwise be difficult to test and verify with traditional methods.
Human Intelligence an Enabler for Cyber Resilience: A Case for Namibian Public Institutions

Government offices, ministries, and agencies (OMAs) under the umbrella term Namibian Public Service (NPS) are the nation’s critical infrastructure custodians and concentrated effort to safeguard these institutions from cyber-attacks is of utmost importance. OMAs are centrally connected through a complex network which allows for intercommunication among the various institutions in the Namibian public service, through this interconnectedness the attack surface is increased as a weakness in the security posture of one institution has the potential to compromise another institution causing a cascading effect. OMAs need to have good cybersecurity hygiene therefore there is a need for a comprehensive guide that takes into consideration the governance, technology, and processes aspect of security hence the need for a cybersecurity resilience model, this paper presents a model design that can be used to equip employees within the public sector to be aware of good security practices thereby become resilient to cyber-attacks. To achieve this, the researchers scrutinised the cybersecurity practices in the sampled OMAs and then designed a cybersecurity resilience model. The study adopted a mixed method approach where a case study of two sites was conducted, the sites were selected because one houses critical infrastructure and the other is the nation’s information technology policy custodian, this enabled the collection of qualitative and quantitative data sets using interviews and surveys. Participants were chosen using a non-probabilistic sampling method, and data was collected and analysed using the concurrent triangulation method to avoid bias. Some key findings from the study were a lack of password management, patch management, ineffective cyber user awareness training, no third-party security vetting mechanism, and overall poor cyber hygiene a sign of inadequacy in handling cyber-attacks. When governance, technology and processes are tackled wholly, it improves security posture. When employees are empowered, cyber resilience can be achieved.
Handling Process Variants in Information Systems with Software Product Line Engineering

Different organisations often run similar business processes at the operational level. Existing approaches define reference or base processes that may be customized to organisation-specific needs to use synergies and reduce costs. However, existing approaches strongly focus on the model perspective rather than on the implementation level of the Process-Aware Information System (PAIS). Software Product Line (SPL) Engineering deals with the implementation and maintenance of similar software products. An SPL comprises common core artefacts based on which a set of similar software products can be derived. This paper demonstrates how to build a PAIS Product Line by applying the concepts of SPL Engineering. The specified and immutable control flow of the PAIS Product Line eliminates dependencies among features, which reduces the challenges of SPL Engineering. A proof-of-concept illustrates the approach.
A Novel Approach Towards Fusion of Steganography and Cryptography for Enhanced Data Security using RGB Image

Security has now become an essential part of communication on both sender and receiver's side. It is challenging to deliver comprehensive security against invaders since a lot of algorithms are expounded and can effortlessly get significant data by performing different hacking methods. High security-based algorithms like AES are nowadays used in various systems, as they present a high performance and is quite faster than most of stream-based encryption algorithms. Regardless of how reliable the technique is, the cipher-text always creates a doubt in brain when someone notices it, which makes the data prone to interception. Here arises another method of security to eliminate that suspicion, hence, we incorporate the confidential information into another format, such as an image, sound, video, or text and then transmit through the network. A novel security system based on three steps of hybrid architecture has been proposed in this paper by combining steganography and cryptography with codebooks. Two different keys are used to encrypt and then hide that highly secured encrypted message into the cover image. The message is encrypted first with AES algorithm and then placed at random positions in the cover image using codebook and KSA algorithm. AES encrypted message is mapped onto values of predefined codebooks then hide those encrypted message bits in LSBs of the cover image. The selected parameters are then passed on to the decryption side to recover the original message. Based on the achieved experimental results, it has outperformed the previous methodologies and without having the secret keys it is unfeasible to extract the message in a readable format.
Crime Detection from Pre-crime Video Analysis with Augmented Pose Information

This study focuses on the task of pre-crime event detection in videos, specifically in the context of shoplifting. While video understanding and anomaly detection in videos have been widely studied, our work proposes a novel approach of utilizing human pose information to augment the pre-crime video data with the aim of predicting critical events such as shoplifting. We used pre-crime scenes from shoplifting videos and normal videos in a 3D CNN architecture, with the addition of pose information as augmented data. The contribution of our study lies in the use of pose information, which captures relevant behaviors of people (such as looking around, walking back and forth, and changing direction) immediately before committing a crime. Our experimental results demonstrate the effectiveness of the proposed method in improving pre-crime event detection accuracy.
Interaction-Aware Labeled Multi-Bernoulli Filter

Tracking multiple objects through time is an important part of an intelligent transportation system. Random finite set (RFS)-based filters are one of the emerging techniques for tracking multiple objects. In multi-object tracking (MOT), a common assumption is that each object is moving independent of its surroundings. However, in many real-world applications, objects interact with one another and the environment. Such interactions are rarely considered within the tracking process. In this paper, we present a novel approach to incorporate target interactions within the prediction step of a RFS-based multi-target filter, i.e. labeled multi-Bernoulli (LMB) filter. The proposed method is capable of explicitly incorporating the effect of target interactions within the filtering process, with little to no changes made for specific applications. We have developed the proposed filter for two practical applications, i.e. tracking a coordinated swarm and vehicles. The method has been tested for a complex vehicle tracking dataset and compared with the PHD filter and LMB filter through the OSPA metric, and with the LMB filter through OSPA(2) metric and cardinality error. The results demonstrate that the proposed interaction-aware method depicts considerable performance enhancement over the other methods in terms of the selected metrics.
Reversing Cyber Loneliness

After more than thirty years we reopen the folder of immersivity in a social dimension enabling creatives to express themselves in open public spaces. Is “immersivity” an interesting and innovative “communicative experience” to be enjoyed together with other citizens, breaking cyber-isolation. This paper describes a interdisciplinary Horizon Europe research project aimed to design and develop a cost effective, hassle-free, easy to install and tune solution to setup a multi-user, multisite, multi-platform non-invasive immersive and interactive users’ experiences, sorry for the long list of buzzwords identifying the key characteristics. The “users” are subdivided in two families: creative/authors and citizens. The first family is again subdivided by: authors without cyber skills and the couple creatives/programmers. The outcomes of the project includes specific training courses to enable both authors to express their creativity thanks to a simple procedure and already developed components or in case of cyber skilled users to integrate the open source platform with new ad-hoc created components.
Using Blockchain, RAID, &amp; BitTorrent Technologies to Secure Digital Evidence from Ransomware

Digital evidence is an important part of solving and prosecuting crimes. However, most the storage systems used are out-of-date and on unsecure networks. Attackers often exploit these system vulnerabilities through the use of ransomware. The recent COVID pandemic has seen a drastic rise in these types of attacks. If an attacker is able to ransom a system the digital evidence stored in it is locked and no longer available, which creates problems for police officers and prosecutors. To mitigate these attacks from destroying months or years of generated digital evidence, the use of blockchain, RAID network storage, and BitTorrent technologies are proposed. Blockchains are public or private ledgers made up of nodes. Each node contains a full copy of the ledger and secures it from tampering by a hash that points to the previous block in the chain. To keep the blockchain from getting too large and slowing down the system as more blocks are added to the chain, the use of RAID and BitTorrent technologies will be used to break up the digital evidence that has been generated.