Métodos discriminativos para la optimización de modelos en la Verificación del Hablante

La creciente necesidad de sistemas de autenticación seguros ha motivado el interés de algoritmos efectivos de Verificación de Hablante (VH). Dicha necesidad de algoritmos de alto rendimiento, capaces de obtener tasas de error bajas, ha abierto varias ramas de investigación. En este trabajo proponemos investigar, desde un punto de vista discriminativo, un conjunto de metodologías para mejorar el desempeño del estado del arte de los sistemas de VH. En un primer enfoque investigamos la optimización de los hiper-parámetros para explícitamente considerar el compromiso entre los errores de falsa aceptación y falso rechazo. El objetivo de la optimización se puede lograr maximizando el área bajo la curva conocida como ROC (Receiver Operating Characteristic) por sus siglas en inglés. Creemos que esta optimización de los parámetros no debe de estar limitada solo a un punto de operación y una estrategia más robusta es optimizar los parámetros para incrementar el área bajo la curva, AUC (Area Under the Curve por sus siglas en inglés) de modo que todos los puntos sean maximizados. Estudiaremos cómo optimizar los parámetros utilizando la representación matemática del área bajo la curva ROC basada en la estadística de Wilcoxon Mann Whitney (WMW) y el cálculo adecuado empleando el algoritmo de descendente probabilístico generalizado. Además, analizamos el efecto y mejoras en métricas como la curva detection error tradeoff (DET), el error conocido como Equal Error Rate (EER) y el valor mínimo de la función de detección de costo, minimum value of the detection cost function (minDCF) todos ellos por sue siglas en inglés. En un segundo enfoque, investigamos la señal de voz como una combinación de atributos que contienen información del hablante, del canal y el ruido. Los sistemas de verificación convencionales entrenan modelos únicos genéricos para todos los casos, y manejan las variaciones de estos atributos ya sea usando análisis de factores o no considerando esas variaciones de manera explícita. Proponemos una nueva metodología para particionar el espacio de los datos de acuerdo a estas carcterísticas y entrenar modelos por separado para cada partición. Las particiones se pueden obtener de acuerdo a cada atributo. En esta investigación mostraremos como entrenar efectivamente los modelos de manera discriminativa para maximizar la separación entre ellos. Además, el diseño de algoritimos robustos a las condiciones de ruido juegan un papel clave que permite a los sistemas de VH operar en condiciones reales. Proponemos extender nuestras metodologías para mitigar los efectos del ruido en esas condiciones. Para nuestro primer enfoque, en una situación donde el ruido se encuentre presente, el punto de operación puede no ser solo un punto, o puede existir un corrimiento de forma impredecible. Mostraremos como nuestra metodología de maximización del área bajo la curva ROC es más robusta que la usada por clasificadores convencionales incluso cuando el ruido no está explícitamente considerado. Además, podemos encontrar ruido a diferentes relación señal a ruido (SNR) que puede degradar el desempeño del sistema. Así, es factible considerar una descomposición eficiente de las señales de voz que tome en cuenta los diferentes atributos como son SNR, el ruido y el tipo de canal. Consideramos que en lugar de abordar el problema con un modelo unificado, una descomposición en particiones del espacio de características basado en atributos especiales puede proporcionar mejores resultados. Esos atributos pueden representar diferentes canales y condiciones de ruido. Hemos analizado el potencial de estas metodologías que permiten mejorar el desempeño del estado del arte de los sistemas reduciendo el error, y por otra parte controlar los puntos de operación y mitigar los efectos del ruido

Statistical study of user perception of smart homes during vital signal monitoring with an energy saving algorithm

Sensor networks are deployed in people’s homes to make life easier and more comfortable and secure. They might represent an interesting approach for elderly care as well. This work highlights the benefits of a sensor network implemented in the homes of a group of users between 55 and 75 years old, which encompasses a simple home energy optimization algorithm based on user behavior. We analyze variables related to vital signs to establish users’ comfort and tranquility thresholds. We statistically study the perception of security that users exhibit, differentiating between men and women, examining how it affects the person’s development at home, as well as the reactivity of the sensor algorithm, to optimize its performance. The proposed algorithm is analyzed under certain performance metrics, showing an improvement of 15% over a sensor network under the same conditions. We look at and quantify the usefulness of accurate alerts on each sensor and how it reflects in the users’ perceptions (for men and women separately). This study analyzes a simple, low-cost, and easy-to-implement home-based sensor network optimized with an adaptive energy optimization algorithm to improve the lives of older adults, which is capable of sending alerts of possible accidents or intruders with the highest efficiency

Fostering digital transformation in education: technology enhanced learning from professors’ experiences in emergency remote teaching

In this work, we aim to understand professors’ perception of the key competences as well as the best educational strategies and technological tools to guide digital transformation (DT) in education, according to their experience in emergency remote teaching (ERT). In recent years, technological advancement has driven DT in many areas, with education being among them. ERT due to COVID-19 accelerated this transition. Restrictions and lockdowns forced higher education institutions to adopt remote teaching strategies and tools suited for a digital environment. We surveyed 100 professors from a private Mexican university with 15-month experience of online ERT. We asked them through Likert scale questions to self-evaluate their performance and whether they perceived it to be better in online or hybrid environments compared with face-to-face environments in different aspects. We performed correlation, cluster, and factor analysis to identify the relationships and patterns in their answers. Through open-ended questions, we also asked the participants about the challenges and achievements they experienced, and the educational strategies and technological tools they successfully incorporated during ERT. We also conducted text mining to extract the most relevant information from these answers and validated that they were not polarized with negative sentiment using a large language model. Our results showed social intelligence as an underlying competence for teaching performance was highlighted in the digital environment due to the physical interaction limitations. Participants found success in implementing information and communication technologies, resulting in maintaining student interest and building trust in the online environment. Professors recognized the relevance not only of learning management systems and communication platforms, as expected, but also hardware such as tablets, cameras, and headphones for the successful delivery of education in a digital environment. Technology Enhanced Learning transposes game-based, quizzing practices, and collaborative learning to digital environments. Furthermore, the professors recommended learning-by-doing, flipped learning, problem-based learning, game-based learning, and holistic education as some pedagogical methodologies that were successfully applied in ERT and could be implemented for DT. Understanding the gains concerning teaching learning strategies and technologies that were incorporated during ERT is of the utmost importance for driving DT and its benefits for current and future education

A monitoring-based approach for WSN security using IEEE-802.15.4/6LowPAN and DTLS communication

International audienceIn this paper, we present a monitoring based approach for securing upper layer communications of WSN (Wireless Sensor Networks), the latter using IEEE802.15.4/6LoWPAN stacks and tinyDTLS. The monitoring techniques have been integrated as an extension to the industrial tool MMT (Montimage Monitoring Tool). The MMT-extension verifies that the network is working following a set of security rules that have been defined by ETSI. The security rules check if the protocol stack is working properly. If MMT detects a security rule that was not respected, then it sends an alarm to the system manager so that he can take properly reactive adjustments. We tested each of the security rules in MMT's extension using point-to-point configuration. After all these tests were verified, we tested our MMT-extension using real data gathered from the FIT IoT-LAB platform. The results of these tests shown that our MMT's extension for WSN using IEEE-802.15.4/6LowPAN and DTLS communication is feasible

On the Routing Protocol Influence on the Resilience of Wireless Sensor Networks to Jamming Attacks

In this work, we compare a recently proposed routing protocol, the multi-parent hierarchical (MPH) protocol, with two well-known protocols, the ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR). For this purpose, we have developed a simulator, which faithfully reifies the workings of a given protocol, considering a fixed, reconfigurable ad hoc network given by the number and location of participants, and general network conditions. We consider a scenario that can be found in a large number of wireless sensor network applications, a single sink node that collects all of the information generated by the sensors. The metrics used to compare the protocols were the number of packet retransmissions, carrier sense multiple access (CSMA) inner loop retries, the number of nodes answering the queries from the coordinator (sink) node and the energy consumption. We tested the network under ordinary (without attacks) conditions (and combinations thereof) and when it is subject to different types of jamming attacks (in particular, random and reactive jamming attacks), considering several positions for the jammer. Our results report that MPH has a greater ability to tolerate such attacks than DSR and AODV, since it minimizes and encapsulates the network segment under attack. The self-configuring capabilities of MPH derived from a combination of a proactive routes update, on a periodic-time basis, and a reactive behavior provide higher resilience while offering a better performance (overhead and energy consumption) than AODV and DSR, as shown in our simulation results

Utilization of 5G Technologies in IoT Applications: Current Limitations by Interference and Network Optimization Difficulties—A Review

5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection processes. This paper presents the 5G and IoT technologies, explaining common architectures, typical IoT implementations, and recurring problems. This work also presents a detailed and explained overview of interference in general wireless applications, interference unique to 5G and IoT, and possible optimization techniques to overcome these challenges. This manuscript highlights the importance of addressing interference and optimizing network performance in 5G networks to ensure reliable and efficient connectivity for IoT devices, which is essential for adequately functioning business processes. This insight can be helpful for businesses that rely on these technologies to improve their productivity, reduce downtime, and enhance customer satisfaction. We also highlight the potential of the convergence of networks and services in increasing the availability and speed of access to the internet, enabling a range of new and innovative applications and services

Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning

In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively

Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

Wireless sensor networks (WSN) are useful in medicine for monitoring the vital signs of elderly patients. These sensors allow for remote monitoring of a patient’s state of health, making it easier for elderly patients, and allowing to avoid or at least to extend the interval between visits to specialized health centers. The proposed system is a low-cost WSN deployed at the elderly patient’s home, monitoring the main areas of the house and sending daily recommendations to the patient. This study measures the impact of the proposed sensor network on nine vital sign metrics based on a person’s sleep patterns. These metrics were taken from 30 adults over a period of four weeks, the first two weeks without the sensor system while the remaining two weeks with continuous monitoring of the patients, providing security for their homes and a perception of well-being. This work aims to identify relationships between parameters impacted by the sensor system and predictive trends about the level of improvement in vital sign metrics. Moreover, this work focuses on adapting a reactive algorithm for energy and performance optimization for the sensor monitoring system. Results show that sleep metrics improved statistically based on the recommendations for use of the sensor network; the elderly adults slept more and more continuously, and the higher their heart rate, respiratory rate, and temperature, the greater the likelihood of the impact of the network on the sleep metrics. The proposed energy-saving algorithm for the WSN succeeded in reducing energy consumption and improving resilience of the network

New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks

In this work, two new self-tuning collaborative-based mechanisms for jamming detection are proposed. These techniques are named (i) Connected Mechanism and (ii) Extended Mechanism. The first one detects jamming by comparing the performance parameters with respect to directly connected neighbors by interchanging packets with performance metric information, whereas the latter, jamming detection relays comparing defined zones of nodes related with a collector node, and using information of this collector detects a possible affected zone. The effectiveness of these techniques were tested in simulated environment of a quadrangular grid of 7 × 7, each node delivering 10 packets/sec, and defining as collector node, the one in the lower left corner of the grid. The jammer node is sending packets under reactive jamming. The mechanism was implemented and tested in AODV (Ad hoc On Demand Distance Vector), DSR (Dynamic Source Routing), and MPH (Multi-Parent Hierarchical), named AODV-M, DSR-M and MPH-M, respectively. Results reveal that the proposed techniques increase the accurate of the detected zone, reducing the detection of the affected zone up to 15% for AODV-M and DSR-M and up to 4% using the MPH-M protocol

A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa