Detecting cancer at an initial stage could change the course of the disease's development. A non-invasive examination consists of the liquid biopsy of blood, revealing biomarkers that could provide information about the existence of a tumour or not in the organism. The research touches upon the relevance of DNA fragments, precisely the length of fragments, in the detection of cancer. An in-depth interpretation of the fragment length distribution for predicting the state of a patient as being healthy or sick with cancer was approached. The distribution was explored from four perspectives: the complete fragment length distribution, the size range from 90 to 150 bp, important lengths selected by the feature extraction methods and the Fourier Transform of the initial data. These were input in three machine learning models. Using the fragment lengths between 93 and 98 produced accuracy and AUC scores of over 0.85 for all supervised classification models. Processing the data with the Fourier Transform and using the amplitude of spectrums as features in the Random Forest model resulted in an AUC of 0.99.

Cancer represents a huge challenge in the medical world, necessitating early detection methods to improve treatment outcomes. The field of fragmentomics emerged as a promising option towards developing efficient non-invasive cancer diagnosis tools. By analysing the differences between the cfDNA fragments from blood samples of healthy patients and patients with cancer, this study aims to determine the most important fragmentomics features for cancer detection. The methods present in this work involve extracting features from the cfDNA fragments available in the experimental dataset, applying a pipeline of feature selection techniques that removes the redundant features, training and evaluating a logistic regression and random forest classifiers to differentiate between healthy and diseased samples, and finally extracting the feature weights from the trained models to understand which features contributed the most to the classification task. Filter-based variance thresholding and Correlation-based Feature Selection (CFS) were employed to refine the dataset. Independent t-test and the Mann-Whitney U test are used to calculate the relationship between the cancer and healthy samples. The Pearson correlation coefficient calculates the correlation between each pair of features. The classification performance of the two proposed models is assessed using the train/test split and the nested cross-validation techniques. The evaluation reveals that logistic regression constantly outperforms the random forest and that removing the redundant features increases the performance of both classifiers. Certain genomic bins, mostly on chromosomes 1, 7 and 8, contain significant features for the classification task. These findings suggest that understanding the importance of the fragmentomics features can lead to improved diagnostic tools such as cancer detection based on blood tests.

Recent research has indicated attributes of cell-free DNA (cfDNA) called fragmentomics
as a promising method for late stage cancer detection in a non-invasive manner. The pri-
mary objective of this research is to uncover hidden patterns and interactions that could
enhance the accuracy and sensitivity of blood-based cancer diagnostics. This study explores
the complementarity between three fragmentomics features; fragment length distribution,
and nucleotide fragment end sequence diversity and nucleosome positioning for four dif-
ferent sample groups; breast cancer, colorectal cancer, lung cancer and healthy controls.
Various machine learning techniques such as linear regression were employed to quantify
any complementary relationships between the features

In recent years, a new way of cancer diagnostics has emerged, the analysis of DNA fragments circulating in the blood of cancer patients known as fragmentomics. This DNA, known as cell-free DNA (cfDNA), is an easily available biomarker for cell types. Deducing the tissue origin of cfDNA can reveal anomalies in cell death caused by diseases. That holds great potential in cancer detection and monitoring. The process of establishing the cell composition of a blood sample is called cell deconvolution. This research paper focuses on the comparison of two methods of cell deconvolution. The first one UXM, solves this problem by employing a reference-based technique using a methylation atlas. The second one reference-free cfSort, utilizes a Deep Learning Neural Network to perform the sample analysis. In the paper, however, a simpler architecture was trained due to the difficulties in reproduction. Experiments have been conducted to assess the sensitivity of both methods. Experiments consisted of 5 major cell types together mixed with white blood cell DNA fragments to assess the sensitivity of each method. Furthermore, different metrics such as Pearson's correlation coefficient have been used to determine the accuracy of both methods. In the end, UXM outperformed cfSort in most metrics, including Pearson's correlation coefficient, indicating its superior accuracy in deconvolution tasks. However, cfSort showed potential for higher prediction accuracy with further development and better documentation. The findings highlight the strengths and limitations of both methods. This study suggests that while UXM is currently more reliable, future improvements in cfSort could make it a viable alternative. Continued research is recommended to enhance the accuracy and transparency of these methods, ensuring their effectiveness in real-world healthcare applications.

Nowadays, the popularity of machine learning and artificial intelligence algorithms is very high. A new research direction has emerged where the machine learning algorithms are executed on resource-constrained embedded devices. With the development of the Internet of Things paradigm, these edge devices are deployed in a lot of places. Due to the limited resources of embedded devices, it is difficult to bring machine learning algorithms to them. This is where the on-device split inference comes in. It is possible to distribute the inference between multiple edge devices and the cloud so that the edge devices can execute the inference of complex machine learning models. This paper presents a systematized literature review of papers that focus on on-device split inference for edge devices. The papers are analyzed and compared based on pre-determined questions and displayed based on several categories.

Spectrum sensing is a vital technology for alleviating pressure on the radio spectrum and will become more sophisticated as billions more devices come online. In the future, more advanced techniques utilizing deep learning will sense which parts of the spectrum are available to communicate on. This is a heavily researched area, but few papers demonstrate methods of deploying deep learning on the resource-constrained edge devices that will ultimately use them. One approach called Spectrum Painting augments spectrograms and detects which signals are present with a Convolutional Neural Network. We optimize this method for microcontrollers by simplifying the computation of spectrograms and using TinyML techniques. This results in over 90% accuracy on signals with a high signal-to-noise ratio and a latency of 159 ms on a 64 MHz CPU. Our findings conclude microcontrollers are capable of utilizing deep learning for spectrum sensing, but custom hardware will still be required in a real-world deployment.

Visible light positioning (VLP) systems enable indoor positioning through a deployment of light-emitting diodes (LEDs) as transmitters and photodiodes (PDs) as receivers. A promising approach in VLP involves recording the received signal strength (RSS) to construct fingerprint samples for later use in positioning. However, achieving high accuracy demands a labor-intensive data collection process.

In this study, we propose improvements to a data cleaning and augmentation pipeline. Our improvements focus on preserving more source data during cleaning and data-based LED position estimation for more reliable data augmentation. Experimental results show that our approach maintains comparable positioning accuracy while reducing data collection efforts by over 99%. Furthermore, we conduct experiments to investigate the impact of spatially irregular data collection strategies on positioning accuracy. Finally, we deploy a machine learning model on a microcontroller to demonstrate the practical feasibility of our proposed methods.

The explosive growth in data collection driven by the proliferation of interconnected devices necessitates novel approaches to data processing. Traditional centralised data processing methods are increasingly inadequate due to the sheer volume of data generated. Distributed Tiny Learning (DTL) offers a compelling solution by distributing machine learning tasks across multiple edge devices and processing data locally, thus minimising the need for data transmission to central servers. This approach is particularly beneficial in scenarios with limited network bandwidth and stringent privacy requirements, enhancing data security and compliance with privacy regulations. The advent of 6G networks, with their promise of unprecedented speed, capacity, and reliability, can further amplify the power of DTL. By providing higher bandwidth and lower latency, 6G enables more efficient data processing and communication among edge devices, thereby enhancing the overall performance and scalability of DTL systems. This integration supports real-time decision-making for applications such as autonomous vehicles, smart cities, and healthcare monitoring.

This paper conducts a comprehensive survey of the state-of-the-art in DTL, categorising scientific literature, mapping the ecosystem and tools, and addressing performance, efficiency, and scalability challenges in ultra-low-power devices within a 6G context. Additionally, it implements and benchmarks two DTL algorithms, providing practical insights into their effectiveness and operational viability.

The versatility of the internet enables many applications that play an increasingly bigger role in our society. However, users have little control over the route that their internet traffic takes, which prevents them from controlling who sees their packets and how their traffic is handled. Researchers have proposed an extension to the internet, called the responsible internet, that aims to provide users with control over the route that their internet traffic takes.
Providing this control is the aim of this thesis. Users can control their route by specifying requirements that their route has to fulfill. This thesis defines the Maximum Path Requirement Intersection (MPRI) problem as the problem of finding the route that satisfies as many of the user’s requirements as possible, and this thesis proves that MPRI is NP-hard. Subsequently, both a heuristic to solve the problem in a reasonable amount of time as well as an exact algorithm that guarantees to find the globally best path are introduced. The performance of the heuristic is measured relative to the globally optimal solution given by the exact algorithm. Results show that less features allow the heuristic to have a larger search space, which improves the results; that the runtime of the heuristic scales polynomially in the number of hops between the start and end node; that the heuristic is most effective in graphs that have a power-law degree distribution and least effective in grid-like graphs; and that in a realistic setting the heuristic runs quickly while performing close to optimal.

Byzantine consensus protocols are designed to build resilient systems to achieve consensus under Byzantine settings, maintaining safety guarantees under any network synchrony model and providing liveness in partially or fully synchronous networks.
However, several Byzantine consensus protocols have been shown to violate liveness properties under certain scenarios. Existing testing methods for checking the liveness of consensus protocols check for time-bounded liveness violations, which generate a large number of false positives.

In this thesis, for the first time, we check the liveness of Byzantine consensus protocols by the temperature and lasso detection methods and precisely detail ad-hoc system state abstractions that should be used to test these algorithms.
We provide a theoretical analysis of the recently-published safety and liveness attacks and whether the existing test method is able to detect them.
To investigate the issues, we focus on the streamlined blockchain consensus, particularly the HotStuff protocol family, which has been recently developed for blockchain consensus. Among these protocols, the HotStuff protocol itself is both safe and live under the partial synchrony assumption, whereas 2-Phase Hotstuff and Sync HotStuff protocols can violate liveness in subtle fault scenarios.
We implemented our liveness checking methods on top of the Twins automated unit test generator to test the HotStuff protocol family and explored the scenarios with message delay.
Our results indicate that our methods successfully detect all known liveness violations and produce fewer false positives than the bounded liveness checks.

Concurrency bugs are easy to introduce but dif- ficult to detect, especially in implementations of distributed algorithms where concurrency non- determinism is an inherent problem. These bugs may only be identified under very specific order- ings of execution events, making them challenging to reproduce. Controlled concurrency testing tech- niques have been proposed to address the testing challenge, by taking over the scheduling of events, and effectively searching the state space of sched- ules to identify the concurrency bugs.
In this paper, we investigate and compare the per- formance of two concurrency exploration algo- rithms, probabilistic concurrency testing (PCT) and delay bounding strategy on our implementation of the HotStuff consensus protocol, which has been quite popular and was adopted by Meta for its Diem blockchain project. Our results suggest that can in- deed find concurrency bugs more frequently in our HotStuff implementation than the baseline random strategy. However, using different bound parame- ters for PCT and delay bounding does not have sig- nificant effect on bug finding performance on our benchmarks.

Consensus algorithms, as well as distributed systems in general, are vulnerable to concurrency bugs due to non-determinism. Such bugs are hard to detect since it is necessary to test using a lot of different scenarios and even then, there is no guarantee to find one.

Controlled concurrency testing is a proposed solution to that problem. Its main strength is allowing for more control over the order in which threads are executed, using controlled scheduling.

In this paper, we utilize Coyote, a concurrency testing framework, to test for concurrency bugs in PBFT, the seminal consensus algorithm. Random Walk, Delay-Bounding, Probabilistic Concurrency Testing (PCT) and Q-Learning are the exploration strategies we performed experiments with. The goal is to find schedules that lead to concurrency bugs. We test for 2 bugs that are seeded by us into the algorithm.

A comparison of the results shows that fair exploration strategies outperform their unfair variations. Finally, we conclude that PCT and fair PCT are the best-performing strategies for the benchmarks we use.

The testing of consensus systems has received growing attention and recent testing tools generate many faulty executions. However, there is a lack of methods that automatically analyze these outputs to identify the root causes of the bugs they found.
This paper presents Isolation, a statistical bug isolation algorithm that uses message-based predicates to discriminate between different faults. We applied our method to executions of the XRP Ledger blockchain and evaluated its performance. Our comparison shows that Isolation correctly separates bugs by their root cause and consistently outperforms the state-of-the-art with higher F-scores.

Due to trends such as the Internet of Things, there has been a growing number of devices that use wireless technologies for communication. This increase leads to bandwidth limitations that forced researchers to explore other types of wireless communication. One of these alternatives is Visible Light Communication (VLC). VLC encodes data by rapidly changing the intensity of a source emitting light in the visible spectrum. One branch of VLC is Passive VLC. Passive VLC uses ambient light, such as sunlight or ceiling lamps, as a source. An advantage of this approach is the drastically reduced power consumption. Since the light is already present, no energy has to be used to power a source. However, a flaw of Passive VLC is the lower throughput due to limitations in the available modulation devices. This thesis aims to mitigate the limited throughput by creating separate independent bands that can simultaneously transmit data. Taking advantage of the relatively wide visible light spectrum, the use of colour filters will be explored to divide the spectrum into distinct bands where independent data can be transmitted. To keep it as accessible as possible, there is a focus on low-cost filters, reducing computational complexity and real-time communication. A characterisation of low-cost colour filters is presented as well as a channel estimation algorithm to mitigate the shortcomings of inexpensive filters. To reduce the computational overhead, an encoding algorithm is presented that drastically reduces the computation needed to decode data. Ultimately, we provide a physical platform that achieves multi-channel passive VLC by exploiting cost-effective colour filters. The platform allows for simple expansion to greater numbers of channels as well as real-time communication due to the ability to adjust to the surrounding conditions.

Third-party verified credentials (e.g. passports, diplomas) are essential in our daily life. The usage of third-party verified credentials bring us convenience in authentication. The Verifiable Credential (VC) data model is a new standard proposed by the W3C association to ease the expression and verification of third-party verified credentials on the Internet. The issuance and presentation of verifiable credentials are tamper-evident and privacy-preserving by design. However, the current verifiable credential data model lacks an explicit revocation design that guarantees the secure operations of the system. The lack of a revocation mechanism significantly limits the application of verifiable credentials. This thesis studies the revocation mechanisms of existing verifiable credential implementations. The existing revocation mechanisms are either tamper-evident or privacy-preserving. None of them can achieve the two properties together. To evolve the revocation mechanism to be both tamper-evident and privacy-preserving by design, we propose a new method which combines the BBS+ signature, a cryptographic accumulator and the blockchain. Our design enables the verifier to verify the presented credential’s revocation status without compromising the credentials holders’ privacy. We implement a proof-of-concept of our revocation mechanism to show it is practical in the real world. The experimental results show that after adding our revocation mechanism, the presentation time of a five-attribute credential changes from 22.22ms to 62.11ms (+39.89ms), and the verification time changes from 13.36ms to 44.56ms (+31.86ms). Moreover, the scalability analysis shows that our revocation mechanism can satisfy the need for revocation in the real world.

Dynamic programming languages (DPLs), such as Python and Ruby, are often used for their flexibility and fast development. The absence of static typing can lead to runtime exceptions and reduced program understandability. To overcome these problems, some DPLs have introduced optional static typing. Because of the tedious effort of adding type annotations to existing projects, different approaches have been employed to generate type annotations. Static type inference methods are sound in their suggestions, but the dynamic nature of DPLs, combined with insufficient satisfied static dependencies, can cause imprecision. Other proposed approaches used machine learning (ML)-based type inference to predict type annotations. ML-based methods don’t have the limitations of static type inference, however, their performance depends on the training set’s quality and they cannot guarantee type correctness because of their probabilistic techniques. One of such ML-based inference approaches, is the state-of-the-art Type4Py model. Type4Py suffers from some of the same limitations of other learning-based approaches, e.g. it cannot predict types outside of its pre-defined type clusters. To this end, this paper presents hpredict, a tool that combines type prediction of Type4Py’s pre-trained model with static type inference. hpredict runs Type4Py’s learning-based inference and static type inference on different copies of type slots and combines the predictions from both methods. Experiments on the test set of the ManyTypes4Py dataset show that hpredict outperforms Type4Py significantly by 11% regarding Top-10 prediction. The findings of this research, lend evidence that hpredict can increase Type4Py’s general type prediction performance by employing static type inference as well.

Microtask crowdsource workers are negatively influenced, mentally as well as physically, by the repetitive nature of the tasks they perform. Research is ongoing on whether using a gesture-based input technique could mitigate these negative effects. This paper identifies possible ways that using gestures as an alternative input modality could lead to exclusion by analysing survey responses, where n=10. While further research is necessary, there are indications that this could lead to cultural and physical exclusion of certain groups. This paper is not meant to discourage using gestures as an alternative method of input but is solely meant to bring attention to possible risks to take into account.

Microtask crowdsourcing has grown in popularity in recent years. Microtasking is a form of crowdsourcing in which typically small, simple tasks are distributed over the Internet to a large number of people, also known as workers. Workers are highly susceptible to developing musculoskeletal disorders due to prolonged computer use and the monotonous, performance-oriented nature of microtasking. Fortunately, it has been demonstrated that exercise can remedy these health issues. Since some body gestures resemble low-intensity exercise, the use of gestures as input in crowdsourced microtasks has the potential to improve the health of the worker. The purpose of this study was to determine which gestures are effective for controlling microtask workflows in terms of health benefits and usability. In an effort to maximize the positive impact on health, a total of 12 gestures were developed for four distinct microtask workflow elements. Then, we incorporated these gestures into a survey to evaluate the subjective perceptions of usability. On the basis of the survey results, we ranked these gestures for each workflow element and proposed three gesture-command dictionaries optimized for maximum efficiency. Due to the numerous limitations of this study, it is strongly recommended that the outcomes be enhanced. The primary contribution of this study is, therefore, the establishment of new research directions for gestural input in microtasking and in all human-computer interaction.

Researchers at the Delft University of Technology have developed Type4Py: a tool that uses Machine Learning to predict types for Python code. These predictions can be applied by developers to their python code to increase readability and can later be tested by a type-checker for possible type-errors. If a prediction does not return a type-error that prediction is called type-correct. Type4Py has been evaluated by matching its predictions with earlier annotations, also called ground-truth, and has gotten an MRR of 71.7%. However, Type4Py’s predictions have not been evaluated on their typecorrectness. Therefore, I sought out to answer the following research question: How well does Type4Py perform when validated by the static typechecker Mypy? I answered this research question by answering two sub-questions: How many of Type4Py’s predictions are type-correct? And how many of Type4Py’s predictions are type-correct and match ground-truth? I tested a cleaned subset of the ManyTypes4Py dataset with Mypy by running a greedy strategy where I would always pick Type4Py’s prediction with the highest confidence on three different confidence thresholds: 0.25, 0.5 and 0.75 and reached accuracies in terms of typecorrectness of 88%, 91% and 95% for those, respectively. For the case where Type4Py’s predictions matched ground-truth, the predictions on those same thresholds reached accuracies in terms of type-correctness of 95%, 97% and 98%. Comparing this with a similar Type predictor namely, Typilus . Type4Py’s predictions are more typecorrect with a confidence level of at most 50%.

Microtask crowdsourcing workers, also known as crowd workers, perform small tasks known as microtasks.
These people use crowdsourcing platforms to complete these microtasks.
Crowd workers have to work in front of a screen to complete these microtasks, risking musculoskeletal problems and other mental problems.
Their working conditions look similar to desk workers, who are people that work remotely or at the office behind a desk.
This study aims to find the health differences between crowd workers and desk workers.
It will provide a general overview on the subjective well-being, experienced and mental health.
In order to analyze the differences in health, a survey will be deployed on a crowdsourcing platform in order to recruit crowd workers and desk workers will be recruited through snowball sampling.
The questions of the survey are divided into 5 groups, each representing a health category: general health, workspace quality, physical well-being, social well-being and emotional well-being.
For this study 17 crowd workers were recruited and 9 desk workers.
From the results, desk workers are healthier in general, have a healthier workspace because some desk workers work in ergonomically good offices, a healthier physical well-being, a healthier social well-being due to them having colleagues and a better emotional well-being. Crowd workers have a lower level of stress, because of the microtasks being mostly very simple, while desk workers have mentally demanding deadlines and projects to work on.