Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multi‐objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to provide the service and costs for battery aging. In this article, battery participation is investigated on primary frequency reserve markets. Reinforcement learning is applied for the optimization. In previous research, only simplified formulations of battery aging have been used in the reinforcement learning formulation, so it is unclear how the optimizer would perform with a real battery. In this article, a physics‐based battery aging model is used to assess the aging. The contribution of this article is a methodology involving a realistic battery simulation to assess the performance of the trained RL agent with respect to battery aging in order to inform the selection of the weighting of the aging term in the RL reward formula. The RL agent performs day-ahead bidding on the Finnish Frequency Containment Reserves for Normal Operation market, with the objective of maximizing market compensation, minimizing market penalties and minimizing aging costs.

Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances. In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets.

Data centers operations are notoriously energy-hungry, with the computing and cooling infrastructures drawing comparable amount of electrical power to operate. A direction to improve their efciency is to optimizethe cooling, in the sense of implementing cooling infrastructures controlschemes that avoid performing over-cooling of the servers.Towards this direction, this work investigates minimum cost linearquadratic control strategies for the problem of managing air cooled datacenters. We derive a physical and a black box model for a general datacenter, identify this model from real data, and then derive, present andtest in the eld a model based Linear-Quadratic Regulator (LQR) strategy that sets the optimal coolant temperature for each individual coolingunit. To validate the approach we compare the eld tests from the LQR strategy against classical Proportional-Integral-Derivative (PID) controlstrategies, and show through our experiments that it is possible to reducethe energy consumption with respect to the existing practices by severalpoints percent without harming the servers within the data center fromthermal perspectives.

Background: The gearbox and machinery faults prediction are expensive both in terms of repair and loss output in production. These losses or faults may lead to complete machinery or plant breakdown. 

Objective: The goal of this study was to apply advanced machine learning techniques to avoid these losses and faults and replace them with predictive maintenance. To identify and predict the faults in industrial machinery using Machine Learning (ML)  and Deep Learning (DL) approaches. 

Methods: Our study was based on two types of datasets which includes gearbox and rotatory machinery dataset. These datasets were analyzed to predict the faults using machine learning and deep neural network models. The performance of the model was evaluated for both the datasets with binary and multi-classification problems using the different machine learning models and their statistics.

Results: In the case of the gearbox fault dataset with a binary classification problem, we observed random forest and deep neural network models performed equally well, with the highest F1-score and AUC score of around 0.98 and with the least error rate of 7%.  In addition to this, in the case of the multi-classification rotatory machinery fault prediction dataset, the random forest model outperformed the deep neural network model with an AUC score of 0.98. 

Conclusions: In conclusion classification efficiency of the Machine Learning (ML) and Deep Neural Network (DNN) model were tested and evaluated. Our results show Random Forest (RF) and Deep Neural Network (DNN) models have better fault prediction ability to identify the different types of rotatory machinery and gearbox faults as compared to the decision tree and AdaBoost. 

Keywords: Machine Learning, Deep Learning, Big Data, Predictive Maintenance, Rotatory Machinery Fault Prediction, Gearbox Fault Prediction, Machinery Fault Database, Internet of Things (IoT), Spectra quest machinery fault simulator, Cloud Computing, Industry 4.0

The popularity of network virtualization has recently regained considerable momentum because of the emergence of OpenFlow technology. It is essentially decouples a data plane from a control plane and promotes hardware programmability. Subsequently, OpenFlow facilitates the implementation of network virtualization. This study aims to provide an overview of different approaches to create a virtual network using OpenFlow technology. The paper also presents the OpenFlow components to compare conventional network architecture with OpenFlow network architecture, particularly in terms of the virtualization. A thematic OpenFlow network virtualization taxonomy is devised to categorize network virtualization approaches. Several testbeds that support OpenFlow network virtualization are discussed with case studies to show the capabilities of OpenFlow virtualization. Moreover, the advantages of popular OpenFlow controllers that are designed to enhance network virtualization is compared and analyzed. Finally, we present key research challenges that mainly focus on security, scalability, reliability, isolation, and monitoring in the OpenFlow virtual environment. Numerous potential directions to tackle the problems related to OpenFlow network virtualization are likewise discussed

Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.

The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.

Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.

The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.

The utilization of different types of brain images has been expanding, which makes manually examining each image a labor-intensive task. This study introduces a brain tumor segmentation method that uses two parallel U-Net with an asymmetric residual-based deep convolutional neural network (TPUAR-Net). The proposed method is customized to segment high and low grade glioblastomas identified from magnetic resonance imaging (MRI) data. Varieties of these tumors can appear anywhere in the brain and may have practically any shape, contrast, or size. Thus, this study used deep learning techniques based on adaptive, high-efficiency neural networks in the proposed model structure. In this paper, several high-performance models based on convolutional neural networks (CNNs) have been examined. The proposed TPUAR-Net capitalizes on different levels of global and local features in the upper and lower paths of the proposed model structure. In addition, the proposed method is configured to use the skip connection between layers and residual units to accelerate the training and testing processes. The TPUAR-Net model provides promising segmentation accuracy using MRI images from the BRATS 2017 database, while its parallelized architecture considerably improves the execution speed. The results obtained in terms of Dice, sensitivity, and specificity metrics demonstrate that TPUAR-Net outperforms other methods and achieves the state-of-the-art performance for brain tumor segmentation.

Abstract Using prototypes when developing IT artifacts is not something new. With the help of prototypes, developers get a chance to show what the IT artifact can look like without investing too much time or money. The end user also gets the chance to see the IT artifact before it has been developed and gets a chance to add or remove features and requirements. This study examines how to use prototypes and how the use of prototypes improves the experience of mobile application interfaces for young people. In the study, young people from a leisure center were interviewed regarding the development of a mobile application. Three different prototypes have been presented and data has been collected on those prototypes to then see how the different prototypes can be used in a development process. After data collection, it turned out that the Lo-fi prototype did not contribute as much as the Hi-fi prototypes, so there is no need to develop Lo-fi prototypes in order to obtain data on user experience.

An integration of ICT advances into a conventional healthcare system is spreading extensively nowadays. This trend is known as Electronic health or E-Health. E-Health solutions help to achieve the sustainability goal of increasing the expected lifetime while improving the quality of life by providing a constant healthcare monitoring. Cardiovascular diseases are one of the main killers yearly causing approximately 17.7 million deaths worldwide. The focus of this work is on studying the detection of one of the cardiovascular diseases – Atrial Fibrillation (AF) arrhythmia.  This type of arrhythmia has a severe influence on the heart health conditions and could cause congestive heart failure (CHF), stroke, and even increase the risk of death. Therefore, it is important to detect AF as early as possible. In this thesis we focused on studying various machine learning techniques for AF detection using only short single lead Electrocardiography recordings. A web-based solution was built as a final prototype, which first simulates the reception of a recorded signal, conducts the preprocessing, makes a prediction of the AF presence, and visualizes the result. For the AF detection the relatively high accuracy score was achieved comparable to the one of the state-of-the-art. The work was based on the investigation of the proposed architectures and the usage of the database of signals from the 2017 PhysioNet/CinC Challenge. However, an additional constraint was introduced to the original problem formulation, since the idea of a future deployment on the resource-limited devices places the restrictions on the complexity of the computations being performed for achieving the prediction. Therefore, this constraint was considered during the development phase of the project.

In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F ₁ score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.

 Air quality assessment plays a crucial role in understanding the impact of air pollution onhuman health and the environment. With the increasing demand for accurate assessment andprediction of air quality, advanced data analytics modelling techniques offer promisingsolutions. This thesis focuses on leveraging advanced data analytics to assess and analyse airpollution concentration levels in Italy over a 4km resolution using the FORAIR_IT datasetsimulated in ENEA on the CRESCO6 infrastructure, aiming to uncover valuable insights andidentifying the most appropriate AI models for predicting air pollution levels. The datacollection, understanding, and pre-processing procedures are discussed, followed by theapplication of big data training and forecasting using Apache Spark MLlib. The research alsoencompasses different phases, including descriptive and inferential analysis to understand theair pollution concentration dataset, hypothesis testing to examine the relationship betweenvarious pollutants, machine learning prediction using several regression models and anensemble machine learning approach and time series analysis on the entire dataset as well asthree major regions in Italy (Northern Italy – Lombardy, Central Italy – Lazio and SouthernItaly – Campania). The computation time for these regression models are also evaluated and acomparative analysis is done on the results obtained. The evaluation process and theexperimental setup involve the usage of the ENEAGRID/CRESCO6 HPC Infrastructure andApache Spark. This research has provided valuable insights into understanding air pollutionpatterns and improving prediction accuracy. The findings of this study have the potential todrive positive change in environmental management and decision-making processes, ultimatelyleading to healthier and more sustainable communities. As we continue to explore the vastpossibilities offered by advanced data analytics, this research serves as a foundation for futureadvancements in air quality assessment in Italy and the models are transferable to other regionsand provinces in Italy, paving the way for a cleaner and greener future. 

We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work de-scribes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fine-tuning a pre-trained language model. To train the classifier, we obtain positive samples (i.e. high-quality parallel sentences) from a gold-standard curated dataset and extract negative samples (i.e.low-quality parallel sentences) from automatically aligned parallel data by choosing sentences with low alignment scores. Our final machine translation model was then trained on filtered data, instead of the entire noisy dataset. We empirically validate our approach by evaluating on two common datasets and show that data filtering generally improves overall translation quality, in some cases even significantly.

In the past decade, cloud computing has become one of the essential techniques of many business areas, including social media, online shopping, music streaming, and many more. It is difficult for cloud providers to provision their systems in advance due to fluctuating changes in input workload and resultant resource demand. Therefore, there is a need for auto-scaling technology that can dynamically adjust resource allocation of cloud services based on incoming workload. In this paper, we present a predictive auto-scaler for Kubernetes environments to improve the quality of service. Being based on a proactive model, our proposed auto-scaling method serves as a foundation on which to build scalable and resource-efficient cloud systems.

Wireless Sensor Networks (WSN) have been highly developed which can be used in agriculture to enable optimal irrigation scheduling. Since there is an absence of widely used available methods to support effective agriculture practice in different weather conditions, WSN technology can be used to optimise irrigation in the crop fields. This paper presents architecture of an irrigation system by incorporating interoperable IP based WSN, which uses the protocol stacks and standard of the Internet of Things paradigm. The performance of fundamental issues of this network is emulated in Tmote Sky for 6LoWPAN over IEEE 802.15.4 radio link using the Contiki OS and the Cooja simulator. The simulated results of the performance of the WSN architecture presents the Round Trip Time (RTT) as well as the packet loss of different packet size. In addition, the average power consumption and the radio duty cycle of the sensors are studied. This will facilitate the deployment of a scalable and interoperable multi hop WSN, positioning of border router and to manage power consumption of the sensors.

Wireless Sensor Networks (WSNs) are playing remarkable contribution in real time decision making by actuating the surroundings of environment. As a consequence, the contemporary agriculture is now using WSNs technology for better crop production, such as irrigation scheduling based on moisture level data sensed by the sensors. Since WSNs are deployed in constraints environments, the life time of sensors is very crucial for normal operation of the networks. In this regard routing protocol is a prime factor for the prolonged life time of sensors. This research focuses the performances analysis of some clustering based routing protocols to select the best routing protocol. Four algorithms are considered, namely Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP) and Energy Aware Multi Hop Multi Path (EAMMH). The simulation is carried out in Matlab framework by using the mathematical models of those algortihms in heterogeneous environment. The performance metrics which are considered are stability period, network lifetime, number of dead nodes per round, number of cluster heads (CH) per round, throughput and average residual energy of node. The experimental results illustrate that TEEN provides greater stable region and lifetime than the others while SEP ensures more througput.

Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naïve Bayes, Multilayer Perceptron, J48, Naïve Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator’s algorithms. The simulation is carried out by using the WEKA data mining tool.

The thesis focuses on machine learning methods for Earth Observation (EO) data, more specifically, remote sensing data acquired by satellites and drones. EO plays a vital role in monitoring the Earth’s surface and modelling climate change to take necessary precautionary measures. Initially, these efforts were dominated by methods relying on handcrafted features and expert knowledge. The recent advances of machine learning methods, however, have also led to successful applications in EO. This thesis explores supervised and unsupervised approaches of Deep Learning (DL) to monitor natural resources of water bodies and forests. 

The first study of this thesis introduces an Unsupervised Curriculum Learning (UCL) method based on widely-used DL models to classify water resources from RGB remote sensing imagery. In traditional settings, human experts labeled images to train the deep models which is costly and time-consuming. UCL, instead, can learn the features progressively in an unsupervised fashion from the data, reducing the exhausting efforts of labeling. Three datasets of varying resolution are used to evaluate UCL and show its effectiveness: SAT-6, EuroSAT, and PakSAT. UCL outperforms the supervised methods in domain adaptation, which demonstrates the effectiveness of the proposed algorithm. 

The subsequent study is an extension of UCL for the multispectral imagery of Australian wildfires. This study has used multispectral Sentinel-2 imagery to create the dataset for the forest fires ravaging Australia in late 2019 and early 2020. 12 out of the 13 spectral bands of Sentinel-2 are concatenated in a way to make them suitable as a three-channel input to the unsupervised architecture. The unsupervised model then classified the patches as either burnt or not burnt. This work attains 87% F1-Score mapping the burnt regions of Australia, demonstrating the effectiveness of the proposed method. 

The main contributions of this work are (i) the creation of two datasets using Sentinel-2 Imagery, PakSAT dataset and Australian Forest Fire dataset; (ii) the introduction of UCL that learns the features progressively without the need of labelled data; and (iii) experimentation on relevant datasets for water body and forest fire classification. 

This work focuses on patch-level classification which could in future be expanded to pixel-based classification. Moreover, the methods proposed in this study can be extended to the multi-class classification of aerial imagery. Further possible future directions include the combination of geo-referenced meteorological and remotely sensed image data to explore proposed methods. Lastly, the proposed method can also be adapted to other domains involving multi-spectral and multi-modal input, such as, historical documents analysis, forgery detection in documents, and Natural Language Processing (NLP) classification tasks.

This paper introduces a machine learning-based approach for detecting electric poles, an essential part of power grid maintenance. With the increasing popularity of deep learning, several such approaches have been proposed for electric pole detection. However, most of these approaches are supervised, requiring a large amount of labeled data, which is time-consuming and labor-intensive. Unsupervised deep learning approaches have the potential to overcome the need for huge amounts of training data. This paper presents an unsupervised deep learning framework for utility pole detection. The framework combines Convolutional Neural Network (CNN) and clustering algorithms with a selection operation. The CNN architecture for extracting meaningful features from aerial imagery, a clustering algorithm for generating pseudo labels for the resulting features, and a selection operation to filter out reliable samples to fine-tune the CNN architecture further. The fine-tuned version then replaces the initial CNN model, thus improving the framework, and we iteratively repeat this process so that the model learns the prominent patterns in the data progressively. The presented framework is trained and tested on a small dataset of utility poles provided by “Mention Fuvex” (a Spanish company utilizing long-range drones for power line inspection). Our extensive experimentation demonstrates the progressive learning behavior of the proposed method and results in promising classification scores with significance test having p−value<0.00005 on the utility pole dataset.

Massive wildfires not only in Australia, but also worldwide are burning millions of hectares of forests and green land affecting the social, ecological, and economical situation. Widely used indices-based threshold methods like Normalized Burned Ratio (NBR) require a huge amount of data preprocessing and are specific to the data capturing source. State-of-the-art deep learning models, on the other hand, are supervised and require domain experts knowledge for labeling the data in huge quantity. These limitations make the existing models difficult to be adaptable to new variations in the data and capturing sources. In this work, we have proposed an unsupervised deep learning based architecture to map the burnt regions of forests by learning features progressively. The model considers small patches of satellite imagery and classifies them into burnt and not burnt. These small patches are concatenated into binary masks to segment out the burnt region of the forests. The proposed system is composed of two modules: 1) a state-of-the-art deep learning architecture for feature extraction and 2) a clustering algorithm for the generation of pseudo labels to train the deep learning architecture. The proposed method is capable of learning the features progressively in an unsupervised fashion from the data with pseudo labels, reducing the exhausting efforts of data labeling that requires expert knowledge. We have used the realtime data of Sentinel-2 for training the model and mapping the burnt regions. The obtained F1-Score of 0.87 demonstrates the effectiveness of the proposed model.

This paper presents a Convolutional Neural Networks (CNN) based Unsupervised Curriculum Learning approach for the recognition of water bodies to overcome the stated challenges for remote sensing based RGB imagery. The unsupervised nature of the presented algorithm eliminates the need for labelled training data. The problem is cast as a two class clustering problem (water and non-water), while clustering is done on deep features obtained by a pre-trained CNN. After initial clusters have been identified, representative samples from each cluster are chosen by the unsupervised curriculum learning algorithm for fine-tuning the feature extractor. The stated process is repeated iteratively until convergence. Three datasets have been used to evaluate the approach and show its effectiveness on varying scales: (i) SAT-6 dataset comprising high resolution aircraft images, (ii) Sentinel-2 of EuroSAT, comprising remote sensing images with low resolution, and (iii) PakSAT, a new dataset we created for this study. PakSAT is the first Pakistani Sentinel-2 dataset designed to classify water bodies of Pakistan. Extensive experiments on these datasets demonstrate the progressive learning behaviour of UCL and reported promising results of water classification on all three datasets. The obtained accuracies outperform the supervised methods in domain adaptation, demonstrating the effectiveness of the proposed algorithm.

The problem of full-order observer design for a class of delayed nonlinear systems with unknown inputs and Markovian jumping parameters is considered. The design method is formulated as solving a set of linear matrix inequalities (LMI's). Extending the results of nonlinear observer design to Markovian jump systems with time-varying delays is the main advantages of this paper. The sufficient LMI conditions are dependent on both the upper and lower bounds of delay. The effectiveness of the proposed method is shown via an illustrative example.

In this paper, a distributed home automation system will be demonstrated. Traditional systems are based on a central controller where all the decisions are made. The proposed control architecture is a solution to overcome the problems such as the lack of flexibility and re-configurability that most of the conventional systems have. This has been achieved by employing a method based on the new IEC 61499 function block standard, which is proposed for distributed control systems. This paper also proposes a wireless sensor network as the system infrastructure in addition to the function blocks in order to implement the Internet-of-Things technology into the area of home automation as a solution for distributed monitoring and control. The proposed system has been implemented in both Cyber (nxtControl) and Physical (Contiki-OS) level to show the applicability of the solution

A Monte Carlo simulation was used to study the outflow of O+ and H+ ions along three flight trajectories above the polar cap up to altitudes of about 15 RE. Barghouthi (2008) developed a model on the basis of altitude and velocity-dependent wave-particle interactions and a radial geomagnetic field which includes the effects of ambipolar electric field and gravitational and mirror forces. In the present work we improve this model to include the effect of the centrifugal force, with the use of relevant boundary conditions. In addition, the magnetic field and flight trajectories, namely, the central polar cap (CPC), nightside polar cap (NPC), and cusp, were calculated using the Tsyganenko T96 model. To simulate wave-particle interactions, the perpendicular velocity diffusion coefficients for O+ ions in each region were determined such that the simulation results fit the observations. For H+ ions, a constant perpendicular velocity diffusion coefficient was assumed for all altitudes in all regions as recommended by Nilsson et al. (2013). The effect of centrifugal acceleration was simulated by considering three values for the ionospheric electric field: 0 (no centrifugal acceleration), 50, and 100 mV/m. It was found that the centrifugal acceleration increases the parallel bulk velocity and decreases the parallel and perpendicular temperatures of both ion species at altitudes above about 4 RE. Centrifugal acceleration also increases the temperature anisotropy at high altitudes. At a given altitude, centrifugal acceleration decreases the density of H+ ions while it increases the density of O+ ions. This implies that with higher centrifugal acceleration more O+ ions overcome the potential barrier. It was also found that aside from two exceptions centrifugal acceleration has the same effect on the velocities of both ions. This implies that the centrifugal acceleration is universal for all particles. The parallel bulk velocities at a given value of ionospheric electric field were highest in the cusp followed by the CPC followed by the NPC. In this study a region of no wave-particle interaction was assumed in the CPC and NPC between 3.7 and 7.5 RE. In this region the perpendicular temperature was found to decrease with altitude due to perpendicular adiabatic cooling.

This book provides a comprehensive overview of computational intelligence methods for semantic knowledge management. Contrary to popular belief, the methods for semantic management of information were created several decades ago, long before the birth of the Internet. In fact, it was back in 1945 when Vannevar Bush introduced the idea for the first protohypertext: the MEMEX (MEMory + indEX) machine. In the years that followed, Bush’s idea influenced the development of early hypertext systems until, in the 1980s, Tim Berners Lee developed the idea of the World Wide Web (WWW) as it is known today. From then on, there was an exponential growth in research and industrial activities related to the semantic management of the information and its exploitation in different application domains, such as healthcare, e-learning and energy management. 

However, semantics methods are not yet able to address some of the problems that naturally characterize knowledge management, such as the vagueness and uncertainty of information. This book reveals how computational intelligence methodologies, due to their natural inclination to deal with imprecision and partial truth, are opening new positive scenarios for designing innovative semantic knowledge management architectures.

The worldwide electronics market is focusing on developing innovative technologies that can lead to denser, more resilient, and tighter board-level integration. The consumer electronics market is trending toward miniaturization, with HDI-PCBs dominating. Electronics shrinking and scaling technology is the prime concern of all manufacturers. The PCBA industry is transforming its production practices which can reduce the solder joints, limit the usage of discrete and bulky components, reduce the packaging factor of printed boards by accommodating the maximum number of ICs, minimize the assembly span, optimize the latency, and so on. However, developments in production processes in the PCB manufacturing industry need more attention than those in  Silicon-based (ICs) fabrications. One of the issues in PCB fabrication is utilizing conventional metallization approaches. The majority of manufacturers continue to use standard Copper(Cu) laminates on the base substrate and lithography methods to shape the structures.In recent manufacturing technologies, semi-Additive process (SAP) or modified-SAP (mSAP) methods are being adopted to replace traditional subtractive print-and-etch procedures. To scale down the Lines and Spaces (L\&S) on PCBs comparable to that of IC-level, most smartphone makers use Substrate-like PCB (SLP) using mSAP methods. However, subtractive patterning has been used in the intermediate stages of fabrication in those methods. This thesis demonstrates a fully additive selective metallization-based production approach to bridge this technology gap between IC-level and board-level fabrications. The fabrication process has given the name 'Sequential Build-Up Covalent Bonded Metallisation' (SBU-CBM) method.

This dissertation presents a new approach to Cu metallization using a significant step reducing-pattern-transfer process. The patterning method activates a seed layer of CBM polymer chains on a polymer surface with optimal UV-Laser settings. This surface modification enables a strong Copper (Cu) bonding onto the modified surface by Cu-plating. The suggested approach generated a 2.5D surface pattern using a micrometer via laser ablation and subsequent sub-micrometer laser lithography. Furthermore, the surface characterization of each step involved in the fabrication process is analysed and presented to show the sequential growth of layers on top of each other. To investigate the mechanism of the process at the interfaces, characterizations such as EDS, SEM, and XRD characterizations were performed. This PCB manufacturing method can selectively add metallic layers to the finest feature sizes at considerably lower temperatures. Overall, the thesis has addressed two critical aspects i.e. miniaturization of interconnects at board-level and the feasibility of a fully-additive production approach for electronics packaging.

First, a subtractive method is shown to achieve Copper interconnects with feature size 3.0$\mu$m. This miniaturization corresponds to 70\% reduction in the feature size from 20 $\mu$m to 3 $\mu$m. Next, the proposed additive production process has produced Cu interconnects with feature sizes of 2.5 $\mu$m L\&S and via of diameter 10 $\mu$m. The scaling of the interconnects was achieved by optimizing the process parameters involved in the proposed fabrication recipe.

Second, the sequential build-up (SBU) procedure is adopted to realize the embedded passives with the minimum possible feature size ($<$ 10 $\mu$m). An embedded capacitor and a planar inductor were fabricated. The proposed method can be employed to achieve any desirable pattern on FR-4, and a few of them are shown in the thesis. This additive technique can further be investigated through electrical and reliability assessment to make it an industrially accepted method.

Consumer electronics market is escalating towards the miniaturization and the use of HDI-PCBs is dominating. Thus, the production technologies are adapting the Semi-Additive process (SAP) or modified-SAP (mSAP) methods over conventional subtractive print-and-etch methods. Most of the Smartphone manufacturers are using Substrate-like PCB (SLP) with mSAP techniques to scale down the Lines and Spaces (L&S) on PCBs equivalent to ICs. However, those processes still involve subtractive patterning in the intermediate stages of fabrication. In this paper, a fully additive multi-layer patterning process using an electroless copper plating has been investigated. This patterning process is based on modifying a polymer surface by activating a seed layer of grafting polymer chains on it using optimized UV-Laser parameters. This surface modification enables a strong bonding of Copper (Cu) onto the modified surface by Cu-plating. Using a micrometer via laser ablation and subsequent sub-micrometer laser lithography a 2.5D surface pattern has been achieved with the proposed technique.So far, using the proposed additive production process the feature sizes of 2.5 μm L&S and via of diameter 10 μm have been achieved.The via ablation and pattering were done by using 266nm and 375nm laser sources respectively.The substrates used are standard FR4 material and a layer of polyurethane of thickness 35μm coated on top of it. Analysis of the process parameters and their optimization has been done by factorial design method using Design Expert 12.0 software to show their contribution and significance in the production process.

Advancements in production techniques in PCB manufacturing industries are still required as compared to silicon-ICs fabrications. One of the concerned areas in PCBs fabrication is the use of conventional methodologies for metallization. Most of the manufacturers are still using the traditional Copper (Cu) laminates on the base substrate and patterning the structures using lithography processes. As a result, significant amounts of metallic parts are etched away during any mass production process, causing unnecessary disposables leading to pollution. In this work, a new approach for Cu metallization is demonstrated with considerable step-reducing pattern-transfer mechanism. In the fabrication steps, a seed layer of covalent bonded metallization (CBM) chemistry on top of a dielectric epoxy resin is polymerized using actinic radiation intensity of a 375 nm UV laser source. The proposed method is capable of patterning any desirable geometries using the above-mentioned surface modification followed by metallization. To metallize the patterns, a proprietary electroless bath has been used. The metallic layer grows only on the selective polymer-activated locations and thus is called selective metallization. The highlight of this production technique is its occurrence at a low temperature (20–45 °C). In this paper, FR-4 as a base substrate and polyurethane (PU) as epoxy resin were used to achieve various geometries, useful in electronics packaging. In addition, analysis of the process parameters and some challenges witnessed during the process development are also outlined. As a use case, a planar inductor is fabricated to demonstrate the application of the proposed technique.

Miniaturization is the call of the day. Electronics shrinking and scaling technology is the priority of all manufacturers. PCBA Industry is working towards the elimination of solder joints, reduction in use of discrete and bulky components, lowering of assemble span, minimized latency etc. Embedded passive technology is playing a significant role in this roadmap by providing better signal performance, reduced parasitic and crosstalk. In this work, the primary focus is to develop a cost-efficient and flexible fabrication methodology that will be suitable for bulk production. A sequential build up (SBU) procedure is adopted with an additive lithography process to realize the passives with minimum possible feature size (<; 10 μm). A low cost insulating material, promising grafting solution and Laser assisted writing machine with optimized fabrication parameters are the highlights of this production method. A Computer Aided Design (CAD) software i.e. clewin is used during this process to pattern the mask for the entire process. Covalent bonded metallization (CBM) is the key process for the adhesion of copper layer on the desired site of the pattern. In the CBM process, a polymer surface is modified by grafting. The position of the surface modification is optically defined using a laser lithography system. Such surface modified samples are, then treated in an electroless copper process. Resulting in copper metallization only at the locations with a CBM modified surface. The verification of the copper deposition on the substrate is investigated using a high-resolution microscope followed by scanning electron microscopy (SEM). The confirmation of passive formation has been checked using kethley's source (electrical two-probe measurement). The first-order measured results showed the capacitance formed in the range of 0.3-8 pF. Further concrete measurements using standard methods are undergoing. One of the key advantage of this proposed process is its easiness and feasibility of at room temperature.

As per the latest roadmap of iNEMI, the global electronics market is emphasizing to identify disruptive technologies that can contribute towards denser, robust and tighter integration on the board level. Therefore, reduction in packaging factor of printed board can accommodate greater number of ICs to support miniaturization. This paper has shown an experimental method to pattern the metallic layer on a Printed circuit Board (PCB) to the smallest feature size. To investigate this, a commercially available FR-4 PCB with photosensitive material coat and a Copper (Cu) layer on it, is used. A reverse-mode Laser assisted writing is implemented to pattern the desired copper tracks. Soon after, a well-controlled development and chemical etching of the Laser-activated regions are done using Sodium Hydroxide solution followed by an aqueous solution of Sodium Persulfate. Current PCB interconnects used by the industries are of the order (~20 μm). Whereas the present work is a contribution towards achieving Copper interconnects with feature size 3.0μm. This miniaturization corresponds to 70% reduction in the feature size from 20 μm to 3μm. The natural adhesion of the Cu layer has remained intact even after the etching, shows the efficiency of the method adopted. Also, variation in the parameters such as etching time, etchant solution concentrations, temaperature, gain and exposure time of Laser beam and their corresponding effects are discussed. Other highlights of this subtractive method includes its cost-efficiency, lesser production time and repeatability.

To bridge the technology gap between IC-level and board-level fabrications, a fully additive selective metallization has already been demonstrated in the literature. In this article, the surface characterization of each step involved in the fabrication process is outlined with bulk metallization of the surface. This production technique has used polyurethane as epoxy resin and proprietary grafting chemistry to functionalize the surface with covalent bonds on an FR-4 base substrate. The surface was then metalized using an electroless copper (Cu) bath. This sequential growth of layers on top of each other using an actinic laser beam and palladium (Pd) ions to deposit Cu is analyzed. State-of-the-art material characterization techniques were employed to investigate process mechanism at the interfaces. Density functional theory calculations were performed to validate the experimental evidence of covalent bonding of the layers. This manufacturing approach is capable of adding metallic layers in a selective manner to the printed circuit boards at considerably lower temperatures. A complete analysis of the process using bulk deposition of the materials is illustrated in this work.

Despite evidence of rising popularity of video on the web (or VOW), little is known about how users access video. However, such a characterization can greatly benefit the design of multimedia systems such as web video proxies and VOW servers. Hence, this paper presents an analysis of trace data obtained from an ongoing VOW experiment in Lulea University of Technology, Sweden. This experiment is unique as video material is distributed over a high bandwidth network allowing users to make access decisions without the network being a major factor. Our analysis revealed a number of interesting discoveries regarding user VOW access. For example, accesses display high temporal locality: several requests for the same video title often occur within a short time span. Accesses also exhibited spatial locality of reference whereby a small number of machines accounted for a large number of overall requests. Another finding was a browsing pattern where users preview the initial portion of a video to find out if they are interested. If they like it, they continue watching, otherwise they halt it. This pattern suggests that caching the first several minutes of video data should prove effective. Lastly, the analysis shows that, contrary to previous studies, ranking of video titles by popularity did not fit a Zipfian distribution.

Online social media has completely transformed how we communicate with each other. While online discussion platforms are available in the form of applications and websites, an emergent outcome of this transformation is the phenomenon of ‘opinion leaders’. A number of previous studies have been presented to identify opinion leaders in online discussion networks. In particular, Feng (2016 Comput. Hum. Behav. 54, 43–53. (doi:10.1016/j.chb.2015.07.052)) has identified five different types of central users besides outlining their communication patterns in an online communication network. However, the presented work focuses on a limited time span. The question remains as to whether similar communication patterns exist that will stand the test of time over longer periods. Here, we present a critical analysis of the Feng framework both for short-term as well as for longer periods. Additionally, for validation, we take another case study presented by Udanor et al. (2016 Program 50, 481–507. (doi:10.1108/PROG-02-2016-0011)) to further understand these dynamics. Results indicate that not all Feng-based central users may be identifiable in the longer term. Conversation starter and influencers were noted as opinion leaders in the network. These users play an important role as information sources in long-term discussions. Whereas network builder and active engager help in connecting otherwise sparse communities. Furthermore, we discuss the changing positions of opinion leaders and their power to keep isolates interested in an online discussion network.

In this article, we propose a planning algorithm for coverage of complex structures with a network of robotic sensing agents, with multi-robot surveillance missions as our main motivating application. The sensors are deployed to monitor the external surface of a 3D structure. The algorithm controls the motion of each sensor so that a measure of the collective coverage attained by the network is nondecreasing, while the sensors converge to an equilibrium configuration. A modified version of the algorithm is also provided to introduce collision avoidance properties. The effectiveness of the algorithm is demonstrated in a simulation and validated experimentally by executing the planned paths on an aerial robot.

Nowadays, new economic models advocating agility and innovation are emerging in the space sector, this is the “New Space”. New nanosatellites, micro-launchers and other new technologies make it easier to access and use space. For this reason, in December 2019, HEMERIA launched in partnership with CNES its first nanosatellite named Argos Neo on a Generic Economical and Light Satellite (ANGELS), currently in orbit and operational. ANGELS aims to validate the concept of a nanosatellite demonstrator of the platform's equipment and to validate the payload allowing satellite coverage for Argos beacons. Retrieving and analysing satellite telemetry is therefore essential. After studying ANGELS architecture, I developed new and more accurate Python scripts to analyse telemetry (including daily averages instead of monthly averages). Then, I validated and verified Python scripts results thanks to a comparison with current ANGELS activities reports. Finally, I compared Telemetry with the results of electrical and thermal prelaunch analyses (PDR and CDR reviews). Thus, the analysis between the pre-launch studies and Telemetry allowed to identify areas of improvements providing useful feedback for the new KINEIS constellation development.

We take a step towards addressing the under-representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages.

This thesis presents resources capable of enhancing solutions of some Natural Language Processing (NLP) tasks, demonstrates the learning of abstractions by deep models through cross-lingual transferability, and shows how deep learning models trained on idioms can enhance open-domain conversational systems. The challenges of open-domain conversational systems are many and include bland repetitive utterances, lack of utterance diversity, lack of training data for low-resource languages, shallow world-knowledge and non-empathetic responses, among others. These challenges contribute to the non-human-like utterances that open-domain conversational systems suffer from. They, hence,have motivated the active research in Natural Language Understanding (NLU) and Natural Language Generation (NLG), considering the very important role conversations (or dialogues) play in human lives. The methodology employed in this thesis involves an iterative set of scientific methods. First, it conducts a systematic literature review to identify the state-of-the-art (SoTA) and gaps, such as the challenges mentioned earlier, in current research. Subsequently, it follows the seven stages of the Machine Learning (ML) life-cycle, which are data gathering (or acquisition), data preparation, model selection, training, evaluation with hyperparameter tuning, prediction and model deployment. For data acquisition, relevant datasets are acquired or created, using benchmark datasets as references, and their data statements are included. Specific contributions of this thesis are the creation of the Swedish analogy test set for evaluating word embeddings and the Potential Idiomatic Expression (PIE)-English idioms corpus for training models in idiom identification and classification. In order to create a benchmark, this thesis performs human evaluation on the generated predictions of some SoTA ML models, including DialoGPT. As different individuals may not agree on all the predictions, the Inter-Annotator Agreement (IAA) is measured. A typical method for measuring IAA is Fleiss Kappa, however, it has a number of shortcomings, including high sensitivity to the number of categories being evaluated. Therefore, this thesis introduces the credibility unanimous score (CUS), which is more intuitive, easier to calculate and seemingly less sensitive to changes in the number of categories being evaluated. The results of human evaluation and comments from evaluators provide valuable feedback on the existing challenges within the models. These create the opportunity for addressing such challenges in future work. The experiments in this thesis test two hypothesis; 1) an open-domain conversational system that is idiom-aware generates more fitting responses to prompts containing idioms, and 2) deep monolingual models learn some abstractions that generalise across languages. To investigate the first hypothesis, this thesis trains English models on the PIE-English idioms corpus for classification and generation. For the second hypothesis, it explores cross-lingual transferability from English models to Swedish, Yorùbá, Swahili, Wolof, Hausa, Nigerian Pidgin English and Kinyarwanda. From the results, the thesis’ additional contributions mainly lie in 1) confirmation of the hypothesis that an open-domain conversational system that is idiom-aware generates more fitting responses to prompts containing idioms, 2) confirmation of the hypothesis that deep monolingual models learn some abstractions that generalise across languages, 3) introduction of CUS and its benefits, 4) insight into the energy-saving and time-saving benefits of more optimal embeddings from relatively smaller corpora, and 5) provision of public access to the model checkpoints that were developed from this work. We further discuss the ethical issues involved in developing robust, open-domain conversational systems. Parts of this thesis are already published in the form of peer-reviewed journal and conference articles.

This work highlights some important factors for consideration when developing word vector representations and data-driven conversational systems. The neural network methods for creating word embeddings have gained more prominence than their older, count-based counterparts.However, there are still challenges, such as prolonged training time and the need for more data, especially with deep neural networks. Shallow neural networks with lesser depth appear to have the advantage of less complexity, however, they also face challenges, such as sub-optimal combination of hyper-parameters which produce sub-optimal models. This work, therefore, investigates the following research questions: "How importantly do hyper-parameters influence word embeddings’ performance?" and "What factors are important for developing ethical and robust conversational systems?" In answering the questions, various experiments were conducted using different datasets in different studies. The first study investigates, empirically, various hyper-parameter combinations for creating word vectors and their impact on a few natural language processing (NLP) downstream tasks: named entity recognition (NER) and sentiment analysis (SA). The study shows that optimal performance of embeddings for downstream \acrshort{nlp} tasks depends on the task at hand.It also shows that certain combinations give strong performance across the tasks chosen for the study. Furthermore, it shows that reasonably smaller corpora are sufficient or even produce better models in some cases and take less time to train and load. This is important, especially now that environmental considerations play prominent role in ethical research. Subsequent studies build on the findings of the first and explore the hyper-parameter combinations for Swedish and English embeddings for the downstream NER task. The second study presents the new Swedish analogy test set for evaluation of Swedish embeddings. Furthermore, it shows that character n-grams are useful for Swedish, a morphologically rich language. The third study shows that broad coverage of topics in a corpus appears to be important to produce better embeddings and that noise may be helpful in certain instances, though they are generally harmful. Hence, relatively smaller corpus can show better performance than a larger one, as demonstrated in the work with the smaller Swedish Wikipedia corpus against the Swedish Gigaword. The argument is made, in the final study (in answering the second question) from the point of view of the philosophy of science, that the near-elimination of the presence of unwanted bias in training data and the use of foralike the peer-review, conferences, and journals to provide the necessary avenues for criticism and feedback are instrumental for the development of ethical and robust conversational systems.

Building open-domain conversational systems (or chatbots) that produce convincing responses is a recognized challenge. Recent state-of-the-art (SoTA) transformer-based models for the generation of natural language dialogue have demonstrated impressive performance in simulating human-like, single-turn conversations in English.This work investigates, by an empirical study, the potential for transfer learning of such models to Swedish language. DialoGPT, an English language pre-trained model, is adapted by training on three different Swedish language conversational datasets obtained from publicly available sources: Reddit, Familjeliv and the GDC. Perplexity score (an automated intrinsic metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models. We also compare the DialoGPT experiments with an attention-mechanism-based seq2seq baseline model, trained on the GDC dataset. The results indicate that the capacity for transfer learning can be exploited with considerable success. Human evaluators asked to score the simulated dialogues judged over 57% of the chatbot responses to be human-like for the model trained on the largest (Swedish) dataset. The work agrees with the hypothesis that deep monolingual models learn some abstractions which generalize across languages. We contribute the codes, datasets and model checkpoints and host the demos on the HuggingFace platform.

This essay discusses current research efforts in conversational systems from the philosophy of science point of view and evaluates some conversational systems research activities from the standpoint of naturalism philosophical theory. Conversational systems or chatbots have advanced over the decades and now have become mainstream applications. They are software that users can communicate with, using natural language. Particular attention is given to the Alime Chat conversational system, already in industrial use, and the related research. The competitive nature of systems in production is a result of different researchers and developers trying to produce new conversational systems that can outperform previous or state-of-the-art systems. Different factors affect the quality of the conversational systems produced, and how one system is assessed as being better than another is a function of objectivity and of the relevant experimental results. This essay examines the research practices from, among others, Longino’s view on objectivity and Popper’s stand on falsification. Furthermore, the need for qualitative and large datasets is emphasized. This is in addition to the importance of the peer-review process in scientific publishing, as a means of developing, validating, or rejecting theories, claims, or methodologies in the research community. In conclusion, open data and open scientific discussion fora should become more prominent over the mere publication-focused trend.

In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size. Natural language processing (NLP) tasks usually perform better with embeddings from bigger corpora. However, broadness of covered domain and noise can play important roles. We evaluate embeddings based on two Swedish corpora: The Gigaword and Wikipedia, in analogy (intrinsic) tests and discover that the embeddings from the Wikipedia corpus generally outperform those from the Gigaword corpus, which is a bigger corpus. Downstream tests will be required to have a definite evaluation.

In this paper, we show that embeddings from relatively smaller corpora sometimes outperform thosefrom larger corpora and we introduce a new Swedish analogy test set and make it publicly available.To achieve good performance in Natural Language Processing (NLP) downstream tasks, several factorsplay important roles: dataset size, the right hyper-parameters, and well-trained embeddings. We utilizethe fastText tool for our experiments. We evaluate both the Swedish and English embeddings that wecreated using intrinsic evaluation (including analogy & Spearman correlation) and compare them with2 common, publicly available embeddings. Our English continuous Bag-of-Words (CBoW)-negativesampling embedding shows better performance compared to the publicly available GoogleNews version.We also describe the relationship between NLP and cognitive science. We contribute the embeddings forresearch or other useful purposes by publicly releasing them.

In this paper, our main contributions are that embeddings from relatively smaller corpora can outperform ones from far larger corpora and we present the new Swedish analogy test set. To achieve a good network performance in natural language processing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embeddings. We show that, with the right set of hyper-parameters, good network performance can be reached even on smaller datasets. We evaluate the embeddings at the intrinsic level and extrinsic level, by deploying them on the Transformer in named entity recognition (NER) task and conduct significance tests. This is done for both Swedish and English. We obtain better performance in both languages on the downstream task with far smaller training data, compared to recently released, common crawl versions; and character n-grams appear useful for Swedish, a morphologically rich language.

In this study, we demonstrate that an open-domain conversational system trained on idioms or figurative language generates more fitting responses to prompts containing idioms. Idioms are a part of everyday speech in many languages and across many cultures, but they pose a great challenge for many natural language processing (NLP) systems that involve tasks such as information retrieval (IR), machine translation (MT), and conversational artificial intelligence (AI). We utilized the Potential Idiomatic Expression (PIE)-English idiom corpus for the two tasks that we investigated: classification and conversation generation. We achieved a state-of-the-art (SoTA) result of a 98% macro F1 score on the classification task by using the SoTA T5 model. We experimented with three instances of the SoTA dialogue model—the Dialogue Generative Pre-trained Transformer (DialoGPT)—for conversation generation. Their performances were evaluated by using the automatic metric, perplexity, and a human evaluation. The results showed that the model trained on the idiom corpus generated more fitting responses to prompts containing idioms 71.9% of the time in comparison with a similar model that was not trained on the idiom corpus. We have contributed the model checkpoint/demo/code to the HuggingFace hub for public access.