Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features

In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build user profiles as estimates of users' interest on content (e.g., movie genre, director or cast) and then evaluate the performance of the recommender system as a whole e.g., by their ability to recommend relevant and novel items to the target user. The user profile modelling stage, which is a key stage in content-driven RS is barely properly evaluated due to the lack of publicly available datasets that contain user preferences on content features of items. To raise awareness of this fact, we investigate differences between explicit user preferences and implicit user profiles. We create a dataset of explicit preferences towards content features of movies, which we release publicly. We then compare the collected explicit user feature preferences and implicit user profiles built via state-of-the-art user profiling models. Our results show a maximum average pairwise cosine similarity of 58.07\% between the explicit feature preferences and the implicit user profiles modelled by the best investigated profiling method and considering movies' genres only. For actors and directors, this maximum similarity is only 9.13\% and 17.24\%, respectively. This low similarity between explicit and implicit preference models encourages a more in-depth study to investigate and improve this important user profile modelling step, which will eventually translate into better recommendations

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

Background: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective: This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods: The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results: A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions: Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance

Microstrip-fed 3D-printed H-sectorial horn phased array

A 3D-printed phased array consisting of four H-Sectorial horn antennas of 200 g weight with an ultra-wideband rectangular-waveguide-to-microstrip-line transition operating over the whole LMDS and K bands (24.25–29.5 GHz) is presented. The transition is based on exciting three overlapped transversal patches that radiate into the waveguide. The transition provides very low insertion losses, ranging from 0.30 dB to 0.67 dB over the whole band of operation (23.5–30.4 GHz). The measured fractional bandwidth of the phased array including the transition was 20.8% (24.75–30.3 GHz). The antenna was measured for six different scanning angles corresponding to six different progressive phases α, ranging from 0° to 140° at the central frequency band of operation of 26.5 GHz. The maximum gain was found in the broadside direction α = 0°, with 15.2 dB and efficiency η = 78.5%, while the minimum was found for α = 140°, with 13.7 dB and η = 91.2%.This work was supported by MINISTERIO DE CIENCIA, INNOVACIÓN Y UNIVERSIDADES: PID2019-107885GB-C31/AEI/10.13039/501100011033 and DI-2020.Peer ReviewedPostprint (published version

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods

Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications

Technologies for injection molded antennas for mass production

Tesi en modalitat de compendi de publicacions. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Universitat Politècnica de Catalunya's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.(English) The deployment of 5G antenna infrastructure and the mandatory adoption of anti-collision radars for automotive cars will require large amount of antennas operating in the millimeter and sub-millimeter wavelength. These antennas are usually arrays and the possibility to manufacture the antenna array including the feeding network and the radiating element as a plastic piece reducing the need to use large (Printed Circuit Boards) PCB’s on expensive dielectric substrates, can be an interesting manufacturing technology. In this regard, waveguide-based antennas can be assembled using plastic technology with a proper metallization procedure. They are more scalable in terms of efficiency than microstrip line (ML) antennas and as the number of antennas in the array increases the gain is not reduced due to the losses in the substrate. In this thesis, the industrial challenges of this technology are addressed. A detailed tolerance study by including the plastic manufacturing errors, typically +-0.1mm, is carried out in order to check the feasibility of plastic antennas to address mass production. The antennas will need to be integrated with the radar chipsets, so a transition between the chip and the waveguide-antennas will be presented. These transitions can act as a direct chip-waveguide launcher, potentially reducing the need of using large substrates, hence reducing the cost of the antenna. Also, the need to apply metal coating is also explored to achieve the desired performance. Conventional techniques such as copper electrodeposition is used. The main drawback is that the copper has a lot of difficulties depositing into right angle surfaces. Eventually, these antennas will have to be integrated in the aesthetics of a car, usually behind a plastic radome (with its respective manufacturing errors as well) that will need to be designed and optimized properly in order to introduce the minimum distorsions to the radar. Optimization based on simulators done with commercial electromagnetic softwares like CST is not feasible due to the required large computation time. In this regard an ad-hoc ray-tracing based simulator has been developed to asses radome induced errors in radar performance. All these industrial problems are taken into account from the design stage where the time, price, fabrication tolerances and radiation requirements have to be compromised at the same time increasing dramatically the design complexity.(Español) El despliegue de infraestructura de antenas 5G y la adopción obligatoria de radares anticolisión para automóviles requerirá una gran cantidad de antenas que operen en longitudes de onda milimétricas y submilimétricas. Estas antenas suelen ser agrupaciones y la posibilidad de fabricar la agrupación de antenas, incluida la red de alimentación y el elemento radiante como una pieza de plástico, lo que reduce la necesidad de usar PCB grandes (placas de circuito impreso) en sustratos dieléctricos costosos, puede ser una tecnología de fabricación interesante. En este sentido, las antenas basadas en guía de ondas se pueden ensamblar utilizando tecnología plástica con un procedimiento de metalización adecuado. Son más escalables en términos de eficiencia que las antenas de línea microstrip (ML) y, a medida que aumenta el número de antenas en el arreglo, la ganancia no se reduce debido a las pérdidas en el sustrato. En esta tesis se abordan los retos industriales de esta tecnología. Se lleva a cabo un estudio de tolerancia detallado que incluye los errores de fabricación de plástico, normalmente +- 0,1 mm, para comprobar la viabilidad de las antenas de plástico para hacer frente a la producción en masa. Las antenas deberán integrarse junto con los chips de radar, por lo que se presentará una transición entre el chip y las antenas de guía de ondas. Estas transiciones pueden actuar como una transición directa de chip-guía, lo que podría reducir la necesidad de usar sustratos grandes y, por lo tanto, reducir el costo de la antena. Además, también se explora la necesidad de aplicar un recubrimiento metálico para lograr el rendimiento deseado. Se utilizan técnicas convencionales como la electrodeposición de cobre. El principal inconveniente es que el cobre tiene muchas dificultades para depositarse en superficies en ángulo recto. Eventualmente, estas antenas deberán integrarse en la estética de un automóvil, generalmente detrás de un radomo de plástico (con sus respectivos errores de fabricación también) que deberá diseñarse y optimizarse adecuadamente para introducir las mínimas distorsiones al radar. La optimización basada en simuladores realizados con software electromagnético comercial como CST no es factible debido al gran tiempo de cálculo requerido. En este sentido, se ha desarrollado un simulador basado en trazado de rayos ad-hoc para evaluar los errores inducidos por el radomo en el rendimiento del radar. Todos estos problemas industriales se tienen en cuenta desde la etapa de diseño donde el tiempo, el precio, las tolerancias de fabricación y los requisitos de radiación tienen que verse comprometidos al mismo tiempo que aumentan drásticamente la complejidad del diseño.Postprint (published version

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Advances in Modeling and Management of Urban Water Networks

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with analyses on the transition from surface to pressurized flow, impact of water use reduction on the operation of UDSs, and sediment transport in pressurized pipes. The Special Issue also includes one paper dealing with the hydraulic modeling of an urban river with a complex cross-section

Collected Papers (on Neutrosophic Theory and Applications), Volume VIII

This eighth volume of Collected Papers includes 75 papers comprising 973 pages on (theoretic and applied) neutrosophics, written between 2010-2022 by the author alone or in collaboration with the following 102 co-authors (alphabetically ordered) from 24 countries: Mohamed Abdel-Basset, Abduallah Gamal, Firoz Ahmad, Ahmad Yusuf Adhami, Ahmed B. Al-Nafee, Ali Hassan, Mumtaz Ali, Akbar Rezaei, Assia Bakali, Ayoub Bahnasse, Azeddine Elhassouny, Durga Banerjee, Romualdas Bausys, Mircea Boșcoianu, Traian Alexandru Buda, Bui Cong Cuong, Emilia Calefariu, Ahmet Çevik, Chang Su Kim, Victor Christianto, Dae Wan Kim, Daud Ahmad, Arindam Dey, Partha Pratim Dey, Mamouni Dhar, H. A. Elagamy, Ahmed K. Essa, Sudipta Gayen, Bibhas C. Giri, Daniela Gîfu, Noel Batista Hernández, Hojjatollah Farahani, Huda E. Khalid, Irfan Deli, Saeid Jafari, Tèmítópé Gbóláhàn Jaíyéolá, Sripati Jha, Sudan Jha, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, M. Karthika, Kawther F. Alhasan, Giruta Kazakeviciute-Januskeviciene, Qaisar Khan, Kishore Kumar P K, Prem Kumar Singh, Ranjan Kumar, Maikel Leyva-Vázquez, Mahmoud Ismail, Tahir Mahmood, Hafsa Masood Malik, Mohammad Abobala, Mai Mohamed, Gunasekaran Manogaran, Seema Mehra, Kalyan Mondal, Mohamed Talea, Mullai Murugappan, Muhammad Akram, Muhammad Aslam Malik, Muhammad Khalid Mahmood, Nivetha Martin, Durga Nagarajan, Nguyen Van Dinh, Nguyen Xuan Thao, Lewis Nkenyereya, Jagan M. Obbineni, M. Parimala, S. K. Patro, Peide Liu, Pham Hong Phong, Surapati Pramanik, Gyanendra Prasad Joshi, Quek Shio Gai, R. Radha, A.A. Salama, S. Satham Hussain, Mehmet Șahin, Said Broumi, Ganeshsree Selvachandran, Selvaraj Ganesan, Shahbaz Ali, Shouzhen Zeng, Manjeet Singh, A. Stanis Arul Mary, Dragiša Stanujkić, Yusuf Șubaș, Rui-Pu Tan, Mirela Teodorescu, Selçuk Topal, Zenonas Turskis, Vakkas Uluçay, Norberto Valcárcel Izquierdo, V. Venkateswara Rao, Volkan Duran, Ying Li, Young Bae Jun, Wadei F. Al-Omeri, Jian-qiang Wang, Lihshing Leigh Wang, Edmundas Kazimieras Zavadskas