To bring robots into human everyday life, their capacity for social interaction must increase. One way for robots to acquire social skills is by assigning them the concept of identity. This research focuses on the concept of \textit{Explanation Identity} within the broader context of robots' roles in society, particularly their ability to interact socially and explain decisions. Explanation Identity refers to the combination of characteristics and approaches robots use to justify their actions to humans. Drawing from different technical and social disciplines, we introduce Explanation Identity as a multidisciplinary concept and discuss its importance in Human-Robot Interaction. Our theoretical framework highlights the necessity for robots to adapt their explanations to the user's context, demonstrating empathy and ethical integrity. This research emphasizes the dynamic nature of robot identity and guides the integration of explanation capabilities in social robots, aiming to improve user engagement and acceptance.

Physical chemical milk is an emulsion of milk fat in an aqueous solution of proteins, milk sugar and mineral salts. The high molar conductivity of goat milk samples compared to cow's milk indicates a high content of mineral substances. That goat milk is rich in total proteins is also indicated by the protein content in the samples, which are higher than the cow's milk samples. However, higher fat content was recorded in cow's milk samples, which also results in higher surface tension of cow's milk. The freezing point and refractive index of goat milk are higher compared to literature data and cow milk samples. The acidity of goat's milk comes from the acidic properties of casein, citrate, phosphate, etc. it is lower than cow's milk and is in accordance with literature data. The viscosity of pasteurized goat's milk at all temperatures is also higher than that of cow's milk.

This study aims to investigate the influence of muscle mass on jump height based on the stage of biological maturation. The total sample consisted of 71 male athletes with three years of minimum training experience. The athletes were divided into three groups based on biological maturation: PrePHV, MidPHV, and PostPHV. Vertical jump height was assessed using three tests: the countermovement jump (CMJ), the countermovement jump with arm swing (CMJwas), and the squat jump (SJ). The results of the interaction between muscle mass percentage (MM) and peak height velocity (PHV) indicate that the effect of MM on vertical jump variables is greater in the PrePHV and MidPHV groups compared to the PostPHV group. For the PrePHV and MidPHV groups, there was a significant increase in CMJ [b=.83, t(22)=3.77, p=.001 and b=.92, t(14)=3.70, p=.002, respectively] and SJ [b=1.11, t(22)=4.45, p< .001 and b=1.06, t(14)=3.51, p=.003, respectively] when muscle mass percentage increased by one unit, while no significant increments were apparent for the PostPHV group [b=0.71, t=1.98, p=.058 and b=0.48, t(28)=1.65, p=.111, respectively]. Additionally, when muscle mass percentage increased by one unit, the CMJwas performance significantly increased in the PrePHV [b=1.48, t(22)=4.68, p<.001], MidPHV [b=1.15, t(14)=4.59, p<.001], and PostPHV [b=.97, t(28)=2.52, p=.018] groups. This study substantiates muscle mass as an important predictor of explosive power, demonstrating a more pronounced impact in the PrePHV and MidPHV relative to the PostPHV group. The study points out the importance of considering biological maturation when understanding the relationship between muscle mass and explosive power performance in young athletes.

Summary This study investigates earnings management and its determinants in the agricultural and manufacturing sectors, with the aim of promoting the quality of financial reporting. The sample includes 1,381 actively operating companies in AP Vojvodina, Republic of Serbia, in the period from 2019 to 2021. Earnings management activities are identified using discretionary accruals computed with modified Jones model. Panel data analysis reveals that profitability and company size exert a positive and statistically significant influence on earnings management practices. Conversely, sales growth demonstrates a negative and statistically significant impact on earnings management. Furthermore, the analysis across the studied years indicates statistically significant differences in the prevalence of earnings management practices. However, the study found no significant differences in earnings management practices between the agriculture and manufacturing sectors. The significance of this study lies in its potential to provide valuable insights for investors, regulators, and financial analysts, helping them in making informed decisions. Moreover, it contributes to refinement of financial reporting standards and enforcement mechanisms, and enables a more accurate assessment of the financial health and performance of companies in both industries. The research also endeavors to identify sector-specific factors influencing earnings management dynamics, with the aim of enhancing transparency and optimizing decision-making processes in the financial environment.

In this paper, we demonstrate and introduce a novel Situational Awareness with Event-driven Network Programming Edge Network Application (EdgeApp), designed to optimize network resource utilization during vessel teleoperation in congested port areas. The demonstration is conducted on an open real-life EdgeApp 5G Standalone (SA) and beyond testbed situated at the port of Antwerp-Bruges. Through this showcase, we demonstrate how 5G and beyond services, utilizing an open 5G SA testbed, can enhance vessel teleoperation. The proposed solution dynamically adjusts network configurations, allowing for lower-quality camera feeds during vessel autonomy and higher-quality feeds when in the teleoperation zone. The practical application and benefits are exemplified through visual representations within the testbed environment.

The evolving landscape of 5G Standalone (SA) and beyond networks is being increasingly focused on vertical industries. To unlock the full potential for verticals, it is important to tightly integrate Edge Network Applications (EdgeApps) tailored to vertical use cases, with the 5G SA network, while allowing them to interact with each other at the same time. Such interaction enables more transparency in expressing Quality of Service (QoS) demands from verticals in the form of intent while hiding the network complexity from them. In this paper, we propose two EdgeApps, which by interacting with both User Equipments (UEs) and 5G SA network, are becoming aware of network quality (quality-awareness) and context around UEs (situational-awareness). Such awareness is also enabled by initiatives such as GSMA Open Gateway and CAMARA, where network and IT functionality are exposed to application developers through standardized Application Programming Interfaces (APIs) that abstract the underlying complexity on the telco and IT systems. In this paper, we utilize the Nokia Network as Code (NaC) platform that exposes the capabilities of the Telenet 5G SA network through CAMARA APIs allowing our EdgeApps to dynamically and in real-time create events that trigger changes in QoS levels required by vertical applications. The paper showcases this concept through a case study within the Transport and Logistics (T&L) sector, which is focused on improving the safety and efficiency of remote vessel operation in busy port environments. The overall solution is deployed and tested on the Antwerp 5G SA testbed, which consists of the UEs (vehicle and vessel) and 5G network infrastructure in the Port of Antwerp-Bruges, as well as the 5G edge where EdgeApps are running. This research contributes to the broader objective of incorporating diverse industrial applications into the 5G and beyond ecosystem, showcasing tangible benefits for vertical industries.

Epilepsy represents a neurological disorder of the brain characterized by repeated seizures. These are sudden abnormality in the brain’s electrical activities that temporarily affect normal brain function. Electroencephalogram (EEG) is one of the main diagnostic tools for monitoring the brain activity of patients with epilepsy. Typically, the detection of epileptic activity is carried out by an expert by analyzing the EEG recordings, but this is a difficult, error prone and time-consuming task. In order to get timely and accurate automatic detection of seizure, various approaches based on both conventional and deep learning techniques were proposed in the literature. The aim of this paper is to present a framework for the automatic detection of epileptic seizure based on the functional connectivity matrix obtained from EEG signals and deep learning. Convolutional neural networks (CNN) were employed because of their capability to learn patterns of neural activities based on brain connectivity represented by connectivity matrix. Obtained results are very promising indicating a potential of this approach as an efficient tool for automated seizure detection based on EEG data.

In today’s technologically-driven world, protecting ICTs (Information and Communication Technologies) is of great importance. Due to the amount of personal data and the obligations of high transaction accuracy, financial institutions such as banks and insurance companies are even more sensitive to data protection. On the business side, ICT is fundamental for day-to-day operations, so investing in ICT is investing in business continuity, operating and resilience. Integration of ISO 27001:2013 and ISO 9001:2015 standards into an organization’s Information Security Management System (ISMS) and Quality Management System (QMS), respectively, further enhances the importance of protecting ICT. It is also important for organizations to implement these standards as a useful baseline for further compliances, such as for example GDPR (General Data Protection Regulation). These standards provide a framework for continually improving management systems in critical areas, which is just one more reason for implementation.

In the field of telecommunications and cloud communications, accurately and in real-time detecting whether a human or an answering machine has answered an outbound call is of paramount importance. This problem is of particular significance during campaigns as it enhances service quality, efficiency and cost reduction through precise caller identification. Despite the significance of the field, it remains inadequately explored in the existing literature. This paper presents an innovative approach to answering machine detection that leverages transfer learning through the YAMNet model for feature extraction. The YAMNet architecture facilitates the training of a recurrent-based classifier, enabling real-time processing of audio streams, as opposed to fixed-length recordings. The results demonstrate an accuracy of over 96% on the test set. Furthermore, we conduct an in-depth analysis of misclassified samples and reveal that an accuracy exceeding 98% can be achieved with the integration of a silence detection algorithm, such as the one provided by FFmpeg.

Many deep-learning computer vision systems analyse objects not previously observed by the system. However, such tasks can be simplified if the objects are marked beforehand. A straightforward method for marking is printing 2D symbols and attaching them to the objects. Selecting these symbols can affect the performance of the CV system, as similar symbols may require extended training time and a larger training dataset. It is possible to find good symbols differentiated by the given neural network easily. Still, there were no efforts to generalise such findings in the literature, and it is not known if the symbols optimal for one network would work just as well in the other. We explored how transferable symbol selection is between the networks. To this end, 30 sets of randomly selected and augmented symbols were classified by-five neural networks. Each network was given the same training dataset and the same training time. Results were ranked and compared, which allowed the identification of networks which performed similarly so that the symbol selection could be generalised between them.

Fully automated chatbots are increasingly being applied in the real-estate industry. Although, they are not completely able replace interaction between the real-estate agents, they can automate customer support, save human resources for qualitative tasks, accelerate operations, and improve business branding. In this paper, a chatbot for real-estate is developed. The chatbot is able to engage clients in meaningful conversations in real-time. It provides a 24/7 service and effectively reduces administrative costs. The architecture and infrastructure overview are presented. The rule matching algorithm is presented and discussed in detail.

ITIL stands for Information Technology Infrastructure Library. ITIL is the most famous framework for managing IT services. ITIL provides guidelines for the implementation of Service Desk solutions in the business environment of any company. The original scientific and professional contribution of this work is proof that the ITIL framework, through the implementation of a software solution that automates its business processes, can help every company in daily work and can improve its basic and additional business processes. Measurements will be made in a real public company in Bosnia and Herzegovina. Also, the software solution itself will be independently developed with its own original programming code only for the purposes of this research.

ITIL is the most accepted framework for the managing of IT services. Incident Management process is the process which is integrated inside ITIL framework and is responsible for: logging, categorization, prioritization and resolving of all incidents. This paper describes the implementation of ITIL Incident Management process inside one public institution in Bosnia and Herzegovina. Detailed implementation is described inside this paper. After successful implementation, measurements are completed in order to check is Incident Management helped organization to improve its performances. The conclusion of the paper described benefits of implementation of ITIL Incident Management inside any type of the organization and future work.

Audio fingerprinting techniques have seen great advances in recent years, enabling accurate and fast audio retrieval even in conditions when the queried audio sample has been highly deteriorated or recorded in noisy conditions. Expectedly, most of the existing work is centered around music, with popular music identification services such as Apple’s Shazam or Google’s Now Playing designed for individual audio recognition on mobile devices. However, the spectral content of speech differs from that of music, necessitating modifications to current audio fingerprinting approaches. This paper offers fresh insights into adapting existing techniques to address the specialized challenge of speech retrieval in telecommunications and cloud communications platforms. The focus is on achieving rapid and accurate audio retrieval in batch processing instead of facilitating single requests, typically on a centralized server. Moreover, the paper demonstrates how this approach can be utilized to support audio clustering based on speech transcripts without undergoing actual speech-to-text conversion. This optimization enables significantly faster processing without the need for GPU computing, a requirement for real-time operation that is typically associated with state-of-the-art speech-to-text tools.