Trust is a crucial aspect when cyber-physical systems where we have to rely on resources and services under ownership of various entities, such as in the case of Edge, Fog and Cloud computing. The DECENTER’s Fog Computing Platform is developed to support Big Data pipelines, which start from the Internet of Things (IoT), such as cameras that provide video-streams for subsequent analysis. It is used to implement Artificial Intelligence algorithms across the Edge-Fog-Cloud computing continuum which provide benefits to applications, including high Quality of Service (QoS), improved privacy and security, lower operational costs and similar. In this article, we present a trust management architecture for DECENTER that relies on the use of blockchain-based Smart Contracts and specifically designed trustless Smart Oracles. The architecture is implemented on Ethereum ledger (testnet) and three trust management scenarios are used for illustration. The scenarios (trust management for cameras, trusted data flow and QoS based computing node selection) are used to present the benefits of establishing trust relationships among entities, services and stakeholders of the platform. --- ----- ----- ----- ----- ----- ----- ----- ----- ------ ----- ----- ----- ---- Beside this very quality publication A', we had the following two similar publications, the first one being also of rang A': 1) Epure Elena Viorica, Compagno Dario, Salinesi Camille, Deneckere Rébecca, Bajec Marko, Žitnik Slavko, Process models of interrelated speech intentions from online health-related conversations, Elsevier Science Publishers; Artificial intelligence in medicine; 2018; Vol. 91; str. 23-38; Impact Factor: 3.574;Srednja vrednost revije / Medium Category Impact Factor: 2.462; A': 1 Being related to the adoption of new beliefs, attitudes and, ultimately, behaviors, analyzing online communication is of utmost importance for medicine. Multiple health care, academic communities, such as information seeking and dissemination and persuasive technologies, acknowledge this need. However, in order to obtain understanding, a relevant way to model online communication for the study of behavior is required. In this paper, we propose an automatic method to reveal process models of interrelated speech intentions from conversations. Specifically, a domain-independent taxonomy of speech intentions is adopted, an annotated corpus of Reddit conversations is released, supervised classifiers for speech intention prediction from utterances are trained and assessed using 10-fold cross validation (multi-class, one-versus-all and multi-label setups) and an approach to transform conversations into well-defined, representative logs of verbal behavior, needed by process mining techniques, is designed. The experimental results show that: (1) the automatic classification of intentions is feasible (with Kappa scores varying between 0.52 and 1); (2) predicting pairs of intentions, also known as adjacency pairs, or including more utterances from even other heterogeneous corpora can improve the predictions of some classes; and (3) the classifiers in the current state are robust to be used on other corpora, although the results are poorer and suggest that the input corpus may not sufficiently capture varied ways of expressing certain speech intentions. The extracted process models of interrelated speech intentions open new views on grasping the formation of beliefs and behavioral intentions in and from speech, but in-depth evaluation of these conversational models is further required. 2) Weiss Gregor, Bajec Marko, Sense classification of shallow discourse relations with focused RNNs, Public Library of Science; PloS one; 2018; Vol. 13, no. 10; str. 1-24; Impact Factor: 2.776;Srednja vrednost revije / Medium Category Impact Factor: 3.734; WoS Understanding the sense of discourse relations that appear between segments of text is essential to truly comprehend any natural language text. Several au
COBISS.SI-ID: 1538278083
This research paper is probably the first one that applies results from computational trust management research to economics phenomena research. More precisely - a new methodological approach is developed aimed at agents technologies deployment in research of economics related phenomena (e.g. the development of agglomerations). --- ----- ----- ----- ----- ----- ----- ----- ----- ------ ----- ----- ----- ---- Beside this very quality type of publication (A') we had the following similar publications - the first one is of type **exceptional publication (A'')**, the second, the third and the fourth of type A': 1) TRČEK, Denis. Trust and reputation management systems : an e-business perspective, (SpringerBriefs in information systems (Print)). [S. l.]: Springer, cop. 2018. Ilustr. ISBN 978-3-319-62374-0. ISBN 3-319-62374-5. https://link.springer.com/book/10.1007/978-3-319-62374-0. [COBISS.SI-ID 1537485507] -- A'' This title represents a sicentific monograph in the area of computational trust management, which was published at the renowned publisher Springer Nature. 2) TRČEK, Denis, TRČEK, Gašper. sonicLamination - from a concept to artistic binding of visual and sound domains by using advanced technology. International journal of arts and technology, ISSN 1754-8853. [Print ed.], 2019, vol. 11, no. 2, str. 219-229, ilustr. [COBISS.SI-ID 1538124483] This paper presents an application of methodological research approaches in computer communications and security to computer based fine arts creations. 3) HUČ, Aleks, VIDRIH, Rajko, TREBAR, Mira. Determination of pears ripening stages based on electrochemical ethylene sensor. IEEE sensors journal. [Print ed.]. 2020, [vol. ], [no. ], [v tisku, str. 1-8], ilustr. ISSN 1530-437X. DOI: 10.1109/JSEN.2020.2975940. [COBISS.SI-ID 5172600], Institute of Electrical and Electronics Engineers; IEEE sensors journal; 2020; [Vol. ], [no. ]; [v tisku, str. 1-8]; Impact Factor: 3.076;Srednja vrednost revije / Medium Category Impact Factor: 2.251 Sensors represent an increasingly user-friendly data collection method that provides a fast way to analyze various stages of fruit ripening. The sensor system was developed to analyze rapidly changing concentrations of ethylene and was recognized as one of the non-destructive methods for obtaining data from sensor electrochemistry. The selected results were thus comparable to the existing methods, which presents further possibilities for inclusion in the basics of integrating the sensor system into the unit of the Internet of Things. 4) RUPNIK, Rok, KUKAR, Matjaž, VRAČAR, Petar, KOŠIR, Domen, PEVEC, Darko, BOSNIĆ, Zoran. AgroDSS : a decision support system for agriculture and farming. Computers and electronics in agriculture, ISSN 0168-1699. [Print ed.], Jun. 2019, vol. 161, str. 260-271, ilustr. https://www.sciencedirect.com/science/article/pii/S0168169917314205?via%3Dihub, doi: 10.1016/j.compag.2018.04.001. [COBISS.SI-ID 1537776323], (Z, A', A1/2) AgroDSS : a decision support system for agriculture and farming Paper introduces a novel system AgroDSS that bridges the gap between agricultural systems and state-of-the-art decision support methodology. The described system is intended for integration into the existing farm management information systems and provides a cloud-based decision support toolbox, allowing farmers to upload their own data, utilize several data analysis methods and retrieve their outputs. The implemented tools include predictive modeling with explanation, accuracy evaluation, time series clustering and decomposition, and structural change detection. They can help users make predictions for simulated scenarios and better understand the dependencies (interactions) within their domain. To build models, AgroDSS uses data which are gathered through IoT system and its sensors. This paper is the result of EU H2020 project AgroIT. 5) DEOKATE, Balu, LAL, Chhagan, TRČEK, Denis, CONTI, Mauro. Mobility-aware cross-layer routing for pe
COBISS.SI-ID: 1538092995
This paper proposes an energy-efficient approximate multiplier which combines radix-4 Booth encoding and logarithmic product approximation. Additionally, a datapath pruning technique is proposed and studied to reduce the hardware complexity of the multiplier. Various experiments were conducted to evaluate the multiplier’s error performance and efficiency in terms of energy and area utilization. The reported results are based on simulations using TSMC-180nm. Also, the applicability of the proposed multiplier is examined in image sharpening and convolutional neural networks. The applicability assessment shows that the proposed multiplier can replace an exact multiplier and deliver up to a 75% reduction in energy consumption and up to a 50% reduction in area utilization. Comparative analysis with the state-of-the-art multipliers indicates the potential of the proposed approach as a novel design strategy for approximate multipliers. When compared to the state-of-the-art approximate non-logarithmic multipliers, the proposed multiplier offers smaller energy consumption with the same level of applicability in image processing and classification applications. On the other hand, some state-of-the-art approximate logarithmic multipliers exhibit lower energy consumption than the proposed multiplier but deliver significant performance degradation for the selected application cases. This study belongs to the research and development of new computing paradigms and is an example of how to replace traditional multiplication operations with energy-efficient approximation methods and use them to process huge amounts of data in convolutional neural networks. --- ----- ----- ----- ----- ----- ----- ----- ----- ------ ----- ----- ----- ---- In addition to this high quality type A' publication, we have two other publications of this type, while the last one is of type ** "Outstanding Achievement" (A'')**: 1) Bulić Patricio, Kojek Gašper, Biasizzo Anton, Data transmission efficiency in bluetooth low energy versions,MDPI; Sensors; 2019; Vol. 19, no. 17; str. 1-17; Impact Factor: 3.031 One important aspect when choosing a Bluetooth Low Energy (BLE) solution is to analyze its energy consumption for various connection parameters and desired throughput to build an optimal low-power Internet-of-Things (IoT) application and to extend the battery life. In this paper, energy consumption and data throughput for various BLE versions are studied. We have tested the effect of connection interval on the throughput and compared power efficiency relating to throughput for various BLE versions and different transactions. The presented results reveal that shorter connection intervals increase throughput for read/write transactions, but that is not the case for the notify and read/write without response transactions. Furthermore, for each BLE version, the energy consumption is mainly dependable on the data volume. The obtained results provide a design guideline for implementing an optimal BLE IoT application. This study belongs to the research and development of new hardware platforms for IoT. This study is an example of how to efficiently connect an embedded system with limited resources to an IoT network. ----- 2) Risojević Vladimir, Rozman Robert, Pilipović Ratko, Češnovar Rok, Bulić Patricio, Accurate indoor sound level measurement on a low-power and low-cost wireless sensor node, MDPI; Sensors; 2018; Vol. 18, no. 7; str. 1-22; Impact Factor: 3.031; Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware pl
COBISS.SI-ID: 1538564035
Like publications 2) and 3) below, this publication is in the field of user modeling using advanced sensor and mobile technologies for authentication, home support, and more. This is a very important segment of research that is enabled by the development of the Internet of Things, especially for preventive medicine. --- ----- ----- ----- ----- ----- ----- ----- ----- ------- ---- ----- ----- ---- In addition to the high quality publication A 'mentioned above, we also had the following related publication - all being type A': VAVPOTIČ, Damjan, ROBNIK ŠIKONJA, Marko, HOVELJA, Tomaž. Exploring the relations between net benefits of IT projects and CIOs perception of quality of software development disciplines. Business & information systems engineering, ISSN 2363-7005. 2019, vol. , no. , str. 1-14,https://link.springer.com/article/10.1007/s12599-019-00612-4, doi: 10.1007/s12599-019-00612-4. [COBISS.SI-ID 1538289859], 1A1 (Z, A', A1/2) In the context of digital transformation paradigm, software development enterprises are under consistent pressure to adapt and improve their management techniques and development processes. The paper proposes a methodology that enables the identification of software development methodology (SDM) discipline quality categories and the evaluation of SDM disciplines’ net benefits which serve as basis for adaptation and improvement. It advances the evaluation of software process quality from single quality category evaluation to multiple quality categories evaluation as proposed by the Kano model. The study results show that different types of Kano quality are present in individual SDM disciplines and that applications of individual SDM disciplines vary considerably in their relation to net benefits of IT projects. Consequently, software process quality evaluation models should start evaluating multiple categories of quality instead of just one and should not assume that the application of every individual SDM discipline has the same effect on the enterprise’s net benefits. 2) TUTA, Jure, JURIČ, Matjaž B. MFAM : Multiple Frequency Adaptive Model-based indoor localization method. Sensors, ISSN 1424-8220, Mar. 2018, vol. 18, no. 4, str. 1-18, ilustr. http://www.mdpi.com/1424-8220/18/4/963, doi: 10.3390/s18040963. [COBISS.SI-ID 1537764803], 3) ŠPRAGER, Sebastijan, JURIČ, Matjaž B. Robust stride segmentation of inertial signals based on local cyclicity estimation. Sensors, ISSN 1424-8220, Apr. 2018, vol. 18, no. 4, str. 1-24, ilustr. http://www.mdpi.com/1424-8220/18/4/1091, doi: 10.3390/s18041091. [COBISS.SI-ID 1537764547]
COBISS.SI-ID: 1538004931
Gene regulatory networks with different topological and/or dynamical properties might exhibit similar behavior. System that is less perceptive for the perturbations of its internal and external factors should be preferred. Methods for sensitivity and robustness assessment have already been developed and can be roughly divided into local and global approaches. Benefits of both families of approaches compose so called ’glocal’ approaches were developed that apply global and local approaches in an effective and rigorous manner. We present a computational approach for ’glocal’ analysis of viable parameter regions in biological models. The methodology is based on the exploration of high-dimensional viable parameter spaces with global and local sampling, clustering and dimensionality reduction techniques. The proposed methodology allows us to efficiently investigate the viable parameter space regions, evaluate the regions which exhibit the largest robustness, and to gather new insights regarding the size and connectivity of the viable parameter regions. We evaluate the proposed methodology on three different synthetic gene regulatory network models, i.e. the repressilator model, the model of the AC-DC circuit and the model of the edge-triggered master-slave D flip-flop. --- ----- ----- ----- ----- ----- ----- ----- ----- ------- ---- ----- ----- ---- In addition to this publication we have the following related quality publication A' (the last one is not A'): 1) Magdevska Lidija, Mraz Miha, Zimic Nikolaj, Moškon, Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks, MihaBioMed Central; BMC bioinformatics; 2018; Vol. 19, no. 333; str. 1-7; Impact Factor: 2.511;Srednja vrednost revije / Medium Category Impact Factor: 2.066; A': 1; WoS Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in different domains. Since measurements are usually prone to errors, approaches dealing with uncertain data, such as fuzzy logic, are especially suitable for this task. Validation methods that help detect overfitting are needed to eliminate inaccurate models. We propose a method to enlarge the validation datasets on which a fuzzy dynamic models can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and compare their accuracy. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational models, while keeping errors of predictions of accurate models small. 2) Demšar Jure, Lebar Bajec Iztok, A hybrid model for simulating grazing herds in real time, Computer animation and virtual worlds; 2020; Vol. 13, no. 1; str. 1-11; Impact Factor: 0.644; WoS Computer simulations of animal groups are usually performed via individual-based modelling, where simulated animals are designed on the level of individuals. With this approach, developers are able to capture behavioural nuances of real animals. However, modelling each individual as its own entity has the downside of having a high computational cost, meaning that individual-based models are usually not suitable for real-time simulations of very large groups. A common alternative approach is flow-based modelling, where the dynamics of animal congregations are designed on the group level. This enables researchers to create real-time simulations of massive phenomena at the cost of biological authenticity. A novel approach called hybrid modelling tries to mix the best of both worlds—precision of individual-based models and speed of flow-based ones. An unknown surrounding hybrid model is the question of their biological authenticity and relevance. In this study, we develop a hybrid model for the simulation of herds of grazing sheep. Through Bayesian data analysis, we show that such an approach can encompass several aspects of real-world sheep behaviour. Our h
COBISS.SI-ID: 1538345411