792 research outputs found
Advances in machine learning algorithms for financial risk management
In this thesis, three novel machine learning techniques are introduced to address distinct
yet interrelated challenges involved in financial risk management tasks. These approaches
collectively offer a comprehensive strategy, beginning with the precise classification of credit
risks, advancing through the nuanced forecasting of financial asset volatility, and ending
with the strategic optimisation of financial asset portfolios.
Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk
assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture
modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed
using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression
model is then applied to predict the probability of default using the heuristically balanced
datasets. The results underscore the effectiveness of our proposed technique, with superior
performance observed in comparison to other imbalanced preprocessing approaches. This
advancement in credit risk classification lays a solid foundation for understanding individual
financial behaviours, a crucial first step in the broader context of financial risk management.
Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a
Triple Discriminator Generative Adversarial Network with a continuous wavelet transform
is proposed. The proposed model has the ability to decompose volatility time series into
signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform
component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a
Generative Adversarial Network consisting of triple Discriminator and Generator networks.
The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised
loss and reconstruction loss as part of its framework. Data from nine financial assets are
employed to demonstrate the effectiveness of the proposed model. This approach not only
enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis.
Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio
optimisation using historical Low, High, and Close prices of assets as input with weights of
assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return
on investment based on deep reinforcement learning. To provide more learning stability in
an online training process, a Markov Differential Sharpe Ratio reward function has been
proposed as the reinforcement learning objective function. Additionally, a Multi-Memory
Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout
a specified trading period. The use of the insights gained from volatility forecasting into
this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving
superior results based on risk-adjusted reward performance measures.
In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the
accuracy of credit risk classification, through the improvement and understanding of market
volatility, to optimisation of investment strategies. These methodologies collectively show
the potential of the use of machine learning to improve financial risk management
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Federated learning (FL) has drawn increasing attention owing to its potential
use in large-scale industrial applications. Existing federated learning works
mainly focus on model homogeneous settings. However, practical federated
learning typically faces the heterogeneity of data distributions, model
architectures, network environments, and hardware devices among participant
clients. Heterogeneous Federated Learning (HFL) is much more challenging, and
corresponding solutions are diverse and complex. Therefore, a systematic survey
on this topic about the research challenges and state-of-the-art is essential.
In this survey, we firstly summarize the various research challenges in HFL
from five aspects: statistical heterogeneity, model heterogeneity,
communication heterogeneity, device heterogeneity, and additional challenges.
In addition, recent advances in HFL are reviewed and a new taxonomy of existing
HFL methods is proposed with an in-depth analysis of their pros and cons. We
classify existing methods from three different levels according to the HFL
procedure: data-level, model-level, and server-level. Finally, several critical
and promising future research directions in HFL are discussed, which may
facilitate further developments in this field. A periodically updated
collection on HFL is available at https://github.com/marswhu/HFL_Survey.Comment: 42 pages, 11 figures, and 4 table
Qualitative analysis of online reviews of users of hospitality services
Савремено друштво се све више ослања на акумулирана мишљења којa могу да пронађу на интернету. Допринос корисника на технолошким платформама омогућава олакшану интеракцију између истомишљеника заједничких интересовања, и на тај начин се олакшава процес доношења одлука. У оквиру окваквог технолошког контекста, организације у услужном сектору попут туризма и гоститељства, морају да се суоче са изазовом управљања садржајима од стране корисника. Маркетиншки стручњаци су нашли начин да искористе овакве интеракције што истиче значај имплементације нових знања у организацијама које ће помоћи у прикупљању, анализирању, тумачењу и управљању онлајн друштвеним утицајима. Предмет истраживања докторске дисертације је квалитативна анализа онлајн рецензија корисника угоститељских услуга у Србији. У поређењу са нумеричким оценама корисника, текстуалне рецензије одражавају задовољство или незадовољство корисника, али на много детаљнији начин јер садрже више информација и на тај начин се стиче реаланији увид у стварна искуства корисника. Поред квалитативне обраде текста рецензија, идентификација врсте и значаја детерминанти задовољства и незадовољства у рецензијама корисника хотелских (у зависности од типа - градски, планински или бањски) и ресторанских услуга је један од главних задатака дисертације. За потребе истраживања прикупљене су рецензије хотела и ресторана у Србији. Коришћена је комбинација квалитативних и квантитативних метода у циљу доказивања постављених хипотеза. Од квалитативних анализа примењене су анализа фреквенције речи, анализа дужине рецензија, анализа сентимента, анализа читљивости и Латентна Дирихлеова Алокација (ЛДА). Од квантитативних метода коришћена је вишеструка регресија за утврђивање међусобних утицаја варијабли. Анализом фреквенције речи издвојене су речи које су се најчешће појављивале у рецензијама хотела и ресторана. Када су у питању хотели, у позитивним рецензијама су се појављивале речи које су се односиле на карактеристичне услуге које се пружају у одређеном типу хотела и садржале су више позитивних описних придева везаних за искуство конзумације. У негативним рецензијама хотела, без обзира на тип, чешће су се појављивали негативни описни придеви и речи које су указивале на материјалне (опипљиве) елементе хотелског производа. У позитивним рецензијама ресторана је такође присутно доста позитивних описних придева, а у негативним рецензијама је наглашен негативни аспект цене услуга у ресторану. Иако су рецензије негативне, у њима је присутно доста позитивних описних придева, што указује на то да је било аспеката услуге којима су били задовољни. Анализа дужине рецензија је показала да се у рецензијама, како хотела тако и ресторана, много више речи и реченица користи за описивање негативног искуства него позитивног. Анализа читљивости је спроведена с циљем утврђивања колико је просечно година формалног образовања неопходно за разумевање рецензија на прво читање. Резултати анализе су показали да вредности индекса читљивости варирају од веома ниског (рецензије које су разумљиве свима) до веома високог (изузетно тешке за разумевање). Просечна вредност индекса читљивости указује да читаоци морају бити завршне године средње школе за разумевање текста на прво читање. Анализом сентимента анализирана су осећања у рецензијама. Распон сентимента варира од екстремно негативних до екстремно позитивних осећања, али највећи број рецензија, како позитивних тако и негативних, садржао је неутрална и позитивна осећања. Анализирајући сентимент у рецензијама ресторана, добијени су слични резултати као и код рецензија хотела. Распон вредности сентимента варира од екстремно негативних до екстремно позитивних осећања, а са порастом оцене, расте и вредност сентимента. Овакви резултати могу указивати на то да, иако су били незадовољни, искуство корисника није праћено негативним осећањима, која су често заслужна за ширење негативних електронских препорука. Применом ЛДА издвојене су детерминанте задовољства и незадовољства услугама у хотелима (у зависности од типа хотела и категорије, као и од типа госта) и ресторанима. Полазећи од претпоставке да се детерминанте задовољства и незадовољства разликују у зависности од типа хотела, категорије и типа госта добијени су резултати који делимично потврђују ове претпоставке. Претпостављено је и да се различите детерминанте утичу на задовољство и незадовољство услугама у ресторанима, што је делимично потврђено. Применом вишеструке регресије тестирани су утицаји техничких карактеристика рецензија (поларитет, читљивост и дужина) на оцене и корисност рецензија. Добијени резултати су потврдили позитивни утицај сентимента и негативни утицај дужине рецензија на оцене корисника код хотелских рецензија, а у случају ресторана нису потврђени претпостављени утицаји. У случају утицаја техничких карактеристика рецензија хотела на корисност није утврђен значајан утицај, док је код рецензија ресторана пронађен позитиван утицај дужине и негативан утицај сентимента на корисност. Резултати добијени у овој дисертацији имају бројне теоријске и практичне импликације на угоститељску делатност. Будући да је задовољство корисника интегрални део угоститељске делатности, идентификоване детерминанте задовољства и незадовољства корисника могу угоститељима помоћи да унапреде своје пословање. На основу утврђеног утицаја техничких карактеристика рецензија на оцену и корисност, угоститељи могу да теже томе да побољшају перформансе рецензија које добијају од корисника, тако што ће, пружањем услуге врхунског квалитета, смањити негативне и дуге рецензије.Savremeno društvo se sve više oslanja na akumulirana mišljenja koja mogu da pronađu na internetu. Doprinos korisnika na tehnološkim platformama omogućava olakšanu interakciju između istomišljenika zajedničkih interesovanja, i na taj način se olakšava proces donošenja odluka. U okviru okvakvog tehnološkog konteksta, organizacije u uslužnom sektoru poput turizma i gostiteljstva, moraju da se suoče sa izazovom upravljanja sadržajima od strane korisnika. Marketinški stručnjaci su našli način da iskoriste ovakve interakcije što ističe značaj implementacije novih znanja u organizacijama koje će pomoći u prikupljanju, analiziranju, tumačenju i upravljanju onlajn društvenim uticajima. Predmet istraživanja doktorske disertacije je kvalitativna analiza onlajn recenzija korisnika ugostiteljskih usluga u Srbiji. U poređenju sa numeričkim ocenama korisnika, tekstualne recenzije odražavaju zadovoljstvo ili nezadovoljstvo korisnika, ali na mnogo detaljniji način jer sadrže više informacija i na taj način se stiče realaniji uvid u stvarna iskustva korisnika. Pored kvalitativne obrade teksta recenzija, identifikacija vrste i značaja determinanti zadovoljstva i nezadovoljstva u recenzijama korisnika hotelskih (u zavisnosti od tipa - gradski, planinski ili banjski) i restoranskih usluga je jedan od glavnih zadataka disertacije. Za potrebe istraživanja prikupljene su recenzije hotela i restorana u Srbiji. Korišćena je kombinacija kvalitativnih i kvantitativnih metoda u cilju dokazivanja postavljenih hipoteza. Od kvalitativnih analiza primenjene su analiza frekvencije reči, analiza dužine recenzija, analiza sentimenta, analiza čitljivosti i Latentna Dirihleova Alokacija (LDA). Od kvantitativnih metoda korišćena je višestruka regresija za utvrđivanje međusobnih uticaja varijabli. Analizom frekvencije reči izdvojene su reči koje su se najčešće pojavljivale u recenzijama hotela i restorana. Kada su u pitanju hoteli, u pozitivnim recenzijama su se pojavljivale reči koje su se odnosile na karakteristične usluge koje se pružaju u određenom tipu hotela i sadržale su više pozitivnih opisnih prideva vezanih za iskustvo konzumacije. U negativnim recenzijama hotela, bez obzira na tip, češće su se pojavljivali negativni opisni pridevi i reči koje su ukazivale na materijalne (opipljive) elemente hotelskog proizvoda. U pozitivnim recenzijama restorana je takođe prisutno dosta pozitivnih opisnih prideva, a u negativnim recenzijama je naglašen negativni aspekt cene usluga u restoranu. Iako su recenzije negativne, u njima je prisutno dosta pozitivnih opisnih prideva, što ukazuje na to da je bilo aspekata usluge kojima su bili zadovoljni. Analiza dužine recenzija je pokazala da se u recenzijama, kako hotela tako i restorana, mnogo više reči i rečenica koristi za opisivanje negativnog iskustva nego pozitivnog. Analiza čitljivosti je sprovedena s ciljem utvrđivanja koliko je prosečno godina formalnog obrazovanja neophodno za razumevanje recenzija na prvo čitanje. Rezultati analize su pokazali da vrednosti indeksa čitljivosti variraju od veoma niskog (recenzije koje su razumljive svima) do veoma visokog (izuzetno teške za razumevanje). Prosečna vrednost indeksa čitljivosti ukazuje da čitaoci moraju biti završne godine srednje škole za razumevanje teksta na prvo čitanje. Analizom sentimenta analizirana su osećanja u recenzijama. Raspon sentimenta varira od ekstremno negativnih do ekstremno pozitivnih osećanja, ali najveći broj recenzija, kako pozitivnih tako i negativnih, sadržao je neutralna i pozitivna osećanja. Analizirajući sentiment u recenzijama restorana, dobijeni su slični rezultati kao i kod recenzija hotela. Raspon vrednosti sentimenta varira od ekstremno negativnih do ekstremno pozitivnih osećanja, a sa porastom ocene, raste i vrednost sentimenta. Ovakvi rezultati mogu ukazivati na to da, iako su bili nezadovoljni, iskustvo korisnika nije praćeno negativnim osećanjima, koja su često zaslužna za širenje negativnih elektronskih preporuka. Primenom LDA izdvojene su determinante zadovoljstva i nezadovoljstva uslugama u hotelima (u zavisnosti od tipa hotela i kategorije, kao i od tipa gosta) i restoranima. Polazeći od pretpostavke da se determinante zadovoljstva i nezadovoljstva razlikuju u zavisnosti od tipa hotela, kategorije i tipa gosta dobijeni su rezultati koji delimično potvrđuju ove pretpostavke. Pretpostavljeno je i da se različite determinante utiču na zadovoljstvo i nezadovoljstvo uslugama u restoranima, što je delimično potvrđeno. Primenom višestruke regresije testirani su uticaji tehničkih karakteristika recenzija (polaritet, čitljivost i dužina) na ocene i korisnost recenzija. Dobijeni rezultati su potvrdili pozitivni uticaj sentimenta i negativni uticaj dužine recenzija na ocene korisnika kod hotelskih recenzija, a u slučaju restorana nisu potvrđeni pretpostavljeni uticaji. U slučaju uticaja tehničkih karakteristika recenzija hotela na korisnost nije utvrđen značajan uticaj, dok je kod recenzija restorana pronađen pozitivan uticaj dužine i negativan uticaj sentimenta na korisnost. Rezultati dobijeni u ovoj disertaciji imaju brojne teorijske i praktične implikacije na ugostiteljsku delatnost. Budući da je zadovoljstvo korisnika integralni deo ugostiteljske delatnosti, identifikovane determinante zadovoljstva i nezadovoljstva korisnika mogu ugostiteljima pomoći da unaprede svoje poslovanje. Na osnovu utvrđenog uticaja tehničkih karakteristika recenzija na ocenu i korisnost, ugostitelji mogu da teže tome da poboljšaju performanse recenzija koje dobijaju od korisnika, tako što će, pružanjem usluge vrhunskog kvaliteta, smanjiti negativne i duge recenzije.Modern society is increasingly relying on the accumulated opinions of its peers that they can find on the Internet. The contribution of consumers on technology platforms enables easier interaction between like-minded people with common interests, and thus facilitates the decision-making process. Within this technological context, service sector organizations such as tourism and hospitality have to face the challenge of consumer-driven content management. Marketing experts have found a way to take advantage of such interactions, which emphasizes the importance of implementing new knowledge in organizations that will help collect, analyze, interpret and manage online social influences. The subject of the doctoral dissertation research is the qualitative analysis of online reviews of consumers of catering services in Serbia. Compared to numerical ratings of users, text reviews reflect customer satisfaction or dissatisfaction but in a much more detailed way because they contatin more information, and thus gain a realistic insight into real consumer experiences. Identifying the type and importance of determinants of satisfaction and dissatisfaction in consumer reviews according to hotel type (city, mountain or spa hotel) is one of the main tasks of the dissertation. For the puroposes of the research, reviews of hotels and restaurants in Serbia were collected. A combination of qualitative and quantitative methods was used in order to prove the set hypotheses. Qualitative analyzes that were applied are word frequency analysis, review length analysis, sentiment analysis, readability analysis and Latent Dirichlet Allocation (LDA). Among the quantitative methods, multiple regression was used to determine the mutual influence of variables. By analyzing the frequency of words, the words that appeared most often in reviews of hotels and restaurants were singled out. When it comes to hotels, positive reviews featured words that referred to the characteristics services provided in a certain type of hotel and contained more positive descriptive adjectives related to the experience of consumption. In negative hotel reviews, regardless of the hotel type, negative descriptive adjectives and words that indicated the material (tangible) elements of the hotel products appeared more often. In the positive reviews of restaurants, there are also a lot of positive descriptive adjectives, and in negative reviews, the negative aspect of the price of restaurant’s services is emphasized. Although the reviews are negative, there are a lot of positive descriptive adjectives in them, indicating that there were aspects of the services that they were satisfied with. The analysis of the length of reviews showed that in the reviews of both hotels and restaurants, many more words and sentences are used to describe a negative expericence than a positive one. A readability analysis was conducted to determine the average number of years of formal education necessary to understand reviews on first reading. The results of analysis showed that the values of the readability index vary form very low (reviews that are understandable to everyone) to very high (extremly difficult to understand). The average value of the readability index indicates that readers must be in their senior years of high school to understand the text on the first reading. Sentiment analysis analyzed the feelings in the reviews. The range of sentiment values varies from extremely negative to extremly positive sentiments, but the largest number of reviews, both positive and negative, contained neutral and positive sentiments. By analyzing sentiment in restaurant reviews, similar resutls were obtained as in hotel reviews. The range of sentiment values vaires from extremely negative to extremely positive sentiments, and as the rating increases, so does the value of the sentiment. Such results may indicate that, although they were dissatisfied, the user experience was not accompanied by negative feeling, which are often responsible for the spread of negative electronic recommendation. Using LDA, the determinants of satisfaction and dissatisfaction with services in hotels (depending on the type of hotel and category, as well as the type of traveler) and restaurants were isolated. Based on the assumption that the determinants of satisfaction and dissatisfaction differ depending on the type of hotel, category and type of travelers, obtained results partially confirm these assumptions. It was assumed that different determinants influence satisfaction and dissatisfaction with restaurant services, which was partially confirmed. By using multiple regression, the effects of the technical characteristics of reviews (polarity, readability and length) on the ratings and helpfulness of the reviews were tested. The obtained results confirmed the positive impact of sentiment and the negative impact of the length of reviews on user rating of hotel reviews. In the case of restaurants, the assumed impacts were not confirmed. In the case of the influence of tecnical characteristics of hotel reviews on reviews helpfulness, no significant influence was found, while in the case of restaurant reviews, a positive influence of length and a negative influence of sentiment on review helpfulness were found. The results obtained in this dissertation have numerous theoretical and practical implications for the hospitality industry. Since customer satisfaction is an integral part of the hospitality business, the identified determinants of customer satisfaction and dissatisfaction can help hoteliers and restauraters improve their business. Based on the established impact of technical characteristics of review on rating and helpfulness, hoteliers and restauraters can strive to improve the performance of reviews they receive from customers by reducing negative and long reviews by providing superior service
Exploiting Process Algebras and BPM Techniques for Guaranteeing Success of Distributed Activities
The communications and collaborations among activities, pro-
cesses, or systems, in general, are the base of complex sys-
tems defined as distributed systems. Given the increasing
complexity of their structure, interactions, and functionali-
ties, many research areas are interested in providing mod-
elling techniques and verification capabilities to guarantee
their correctness and satisfaction of properties. In particular,
the formal methods community provides robust verification
techniques to prove system properties. However, most ap-
proaches rely on manually designed formal models, making
the analysis process challenging because it requires an expert
in the field. On the other hand, the BPM community pro-
vides a widely used graphical notation (i.e., BPMN) to design
internal behaviour and interactions of complex distributed
systems that can be enhanced with additional features (e.g.,
privacy technologies). Furthermore, BPM uses process min-
ing techniques to automatically discover these models from
events observation. However, verifying properties and ex-
pected behaviour, especially in collaborations, still needs a
solid methodology.
This thesis aims at exploiting the features of the formal meth-
ods and BPM communities to provide approaches that en-
able formal verification over distributed systems. In this con-
text, we propose two approaches. The modelling-based ap-
proach starts from BPMN models and produces process al-
gebra specifications to enable formal verification of system
properties, including privacy-related ones. The process mining-
based approach starts from logs observations to automati-
xv
cally generate process algebra specifications to enable veri-
fication capabilities
Graph Neural Network for Air Quality Prediction: A Case Study in Madrid
Air quality monitoring, modelling and forecasting are considered pressing and challenging
topics for citizens and decision-makers, including the government. The tools used to achieve the above goals
vary depending on the opportunities provided by technological development. Much attention is currently
being paid to machine learning and deep learning methods, which, compared to domain knowledge methods,
often perform better in terms of capturing, computing and processing multidimensional information and
complex dependencies. The technique introduced in this work is an Attention Temporal Graph Convolutional
Network based on a combination of Attention, a Gated Recurrent Unit and a Graph Convolutional Network.
In the framework of the current study, it is initially suggested to use the presented approach in the domain
of air quality prediction. The proposed method was tested using air quality, meteorological and traffic
data obtained from the city of Madrid for the periods January-June 2019 and January-June 2022. The
evaluation metrics, including Root Mean Square Error, Mean Absolute Error and Pearson Correlation
Coefficient, confirmed the proposed model’s advantages compared with the reference models (Temporal
Graph Convolutional Network, Long Short-Term Memory and Gated Recurrent Unit)
Evaluating machine learning models in non-standard settings: An overview and new findings
Estimating the generalization error (GE) of machine learning models is
fundamental, with resampling methods being the most common approach. However,
in non-standard settings, particularly those where observations are not
independently and identically distributed, resampling using simple random data
divisions may lead to biased GE estimates. This paper strives to present
well-grounded guidelines for GE estimation in various such non-standard
settings: clustered data, spatial data, unequal sampling probabilities, concept
drift, and hierarchically structured outcomes. Our overview combines
well-established methodologies with other existing methods that, to our
knowledge, have not been frequently considered in these particular settings. A
unifying principle among these techniques is that the test data used in each
iteration of the resampling procedure should reflect the new observations to
which the model will be applied, while the training data should be
representative of the entire data set used to obtain the final model. Beyond
providing an overview, we address literature gaps by conducting simulation
studies. These studies assess the necessity of using GE-estimation methods
tailored to the respective setting. Our findings corroborate the concern that
standard resampling methods often yield biased GE estimates in non-standard
settings, underscoring the importance of tailored GE estimation
Developing Statistical Literacy Through Tasks: An Analysis of Secondary School Mathematics Textbooks
Statistical literacy is an essential competence that people must face in the era of big data and Society 5.0. In Indonesia and several countries, statistics is taught as a list of competencies in mathematics subject in primary and secondary schools. This study aimed to identify whether statistical tasks in higher secondary school mathematics textbooks support statistical literacy. The qualitative data were collected via deductive content analysis, with a specific framework, toward five Compulsory Mathematics textbooks used in Indonesia. We found that statistical exercises and problems in these textbooks were dominated by data analysis type, emphasizing calculating statistics from raw data and its modification. Regarding data visualization, almost all textbooks introduced the histogram and ogive, while some also introduced the boxplot, stem-and-leaf plot, and dot plot. Improvement could be made by adding more exercises and problems related to the interpretation of statistics, evaluation of statistical results, and comparison of statistics from several data groups
Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment
Social media is awash with hateful content, much of which is often veiled
with linguistic and topical diversity. The benchmark datasets used for hate
speech detection do not account for such divagation as they are predominantly
compiled using hate lexicons. However, capturing hate signals becomes
challenging in neutrally-seeded malicious content. Thus, designing models and
datasets that mimic the real-world variability of hate warrants further
investigation.
To this end, we present GOTHate, a large-scale code-mixed crowdsourced
dataset of around 51k posts for hate speech detection from Twitter. GOTHate is
neutrally seeded, encompassing different languages and topics. We conduct
detailed comparisons of GOTHate with the existing hate speech datasets,
highlighting its novelty. We benchmark it with 10 recent baselines. Our
extensive empirical and benchmarking experiments suggest that GOTHate is hard
to classify in a text-only setup. Thus, we investigate how adding endogenous
signals enhances the hate speech detection task. We augment GOTHate with the
user's timeline information and ego network, bringing the overall data source
closer to the real-world setup for understanding hateful content. Our proposed
solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that
enriches the linguistic subspace with latent endogenous signals from history,
topology, and exemplars. HEN-mBERT transcends the best baseline by 2.5% and 5%
in overall macro-F1 and hate class F1, respectively. Inspired by our
experiments, in partnership with Wipro AI, we are developing a semi-automated
pipeline to detect hateful content as a part of their mission to tackle online
harm.Comment: 15 pages, 4 figures, 11 tables. Accepted at SIGKDD'2
Current Challenges in the Application of Algorithms in Multi-institutional Clinical Settings
The Coronavirus disease pandemic has highlighted the importance of artificial intelligence in multi-institutional clinical settings. Particularly in situations where the healthcare system is overloaded, and a lot of data is generated, artificial intelligence has great potential to provide automated solutions and to unlock the untapped potential of acquired data. This includes the areas of care, logistics, and diagnosis. For example, automated decision support applications could tremendously help physicians in their daily clinical routine. Especially in radiology and oncology, the exponential growth of imaging data, triggered by a rising number of patients, leads to a permanent overload of the healthcare system, making the use of artificial intelligence inevitable. However, the efficient and advantageous application of artificial intelligence in multi-institutional clinical settings faces several challenges, such as accountability and regulation hurdles, implementation challenges, and fairness considerations. This work focuses on the implementation challenges, which include the following questions: How to ensure well-curated and standardized data, how do algorithms from other domains perform on multi-institutional medical datasets, and how to train more robust and generalizable models? Also, questions of how to interpret results and whether there exist correlations between the performance of the models and the characteristics of the underlying data are part of the work. Therefore, besides presenting a technical solution for manual data annotation and tagging for medical images, a real-world federated learning implementation for image segmentation is introduced. Experiments on a multi-institutional prostate magnetic resonance imaging dataset showcase that models trained by federated learning can achieve similar performance to training on pooled data. Furthermore, Natural Language Processing algorithms with the tasks of semantic textual similarity, text classification, and text summarization are applied to multi-institutional, structured and free-text, oncology reports. The results show that performance gains are achieved by customizing state-of-the-art algorithms to the peculiarities of the medical datasets, such as the occurrence of medications, numbers, or dates. In addition, performance influences are observed depending on the characteristics of the data, such as lexical complexity. The generated results, human baselines, and retrospective human evaluations demonstrate that artificial intelligence algorithms have great potential for use in clinical settings. However, due to the difficulty of processing domain-specific data, there still exists a performance gap between the algorithms and the medical experts. In the future, it is therefore essential to improve the interoperability and standardization of data, as well as to continue working on algorithms to perform well on medical, possibly, domain-shifted data from multiple clinical centers
Interactive visualisation of electricity usage in smart environments
Saving electricity is a trending topic due to the electricity challenges that are being faced globally. Smart environments are environments that are equipped with physical objects, which include computers, sensors, actuators, smartphones, and wearable devices interconnected together through the Internet of Things. The Internet of Things provides a network to achieve communication, and computation abilities to provide individuals with smart services anytime, and anywhere. Rapid developments in information technology have increased the number of smart appliances being used, leading to increased electricity usage. Devices and appliances in Smart Environments continue to consume electricity even when not in use, because of the standby function. The problems arise as the electricity consumption of the standby function accumulates to large amounts. Effective communication through visualisation of the electricity consumption in a Smart Environment provides a viable solution to reducing the consumption of electricity. This research aimed to design and developed a visualisation system that successfully communicates electricity consumption to the user using a variety of visualisation techniques. The Design Science Research Methodology was used to address the research questions and was used to iteratively design and develop an energy usage visualisation system. The visualisation system was created for the Smart Lab at the Nelson Mandela University's Department of Computing Sciences. A usability study was conducted to assess the usability and efficacy of the system. The system was found to be usable and effective in communicating power usage to potential customers, since the participants were able to complete the tasks in a short amount of time. The positive results show that visualisation can aid in communicating electricity usage to customers, resulting in a possible reduction in electricity consumption and improved decision-making.Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202
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