Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive HVAC Comfort Concept for a Hot and Humid Climate

Worldwide, today’s buildings require massive energy input for air conditioning in terms of heating, cooling and de-/humidification, including auxiliary energy demand for the mechanical system itself. The way we define “comfortable” thermal conditions plays a significant role in this environmental impact. Adaptive comfort models, developed on the basis of field studies in tropical and subtropical climates, give evidence that extended operative temperature and humidity ranges are acceptable for indoor climate especially when combined with elevated air speed controlled by the occupants. Adaptive comfort can typically be achieved with fewer mechanical systems. In this context, the present work was embarked upon with the overall objective of developing a comfort calculation methodology that evaluates thermal comfort on the basis of dynamical thermal building simulation, considering the effect of air movement under hot and humid thermal conditions. Theoretical analysis and comparison study of thermal comfort indices are presented. The Standard Effective Temperature (SET) and the Predicted Mean Vote for elevated air speed (PMVeas) according to the ASHRAE Standard 55-2013 are proposed as preferred indices for evaluating the effect of air movement in a tropical climate. A calculation method is developed, implementing SET and PMVeas, and variably adapting their input parameters for thermal comfort regulation. The dynamical building simulation program TRNSYS 17 3D in combination with the developed calculation method is used for elaborating comfort design strategies for a generic classroom model in the hot and humid summer climate of Dhaka, Bangladesh. The designs are compared with regard to achieved comfort, required systems and energy demand. Following this, an adaptive Heating, Ventilation and Air Conditioning (adaptive HVAC) design is proposed as an alternative strategy to deterministic HVAC control systems with a static “comfort zone”. In this context, ‘adaptive’ does not imply changing people’s comfort expectations. On the contrary it means calculating environmental parameters, especially the air speed, by a new evaluation method to create comfort. The adaptive HVAC comfort design provides fresh air quality and good thermal comfort: A simple decentralized mechanical system supplies the room with 20°C air temperature, while dehumidifying the air to 20°C dew point temperature. In addition, ceiling fans create an average air movement at 0.7 m/s. Compared to the conventional concept of returned air with full heat recovery, aiming at 26°C operative room temperature, the energy demand is only about 75%. Both approaches provide the same comfort according to PMV for elevated air speed. This means that 25% energy savings can be achieved without compromising the thermal comfort of occupants.
Domain-independent reward machines for modular integration of planning and learning

Integrating planning and learning components has many advantages in practical applications, as it allows for combining the different beneﬁts of the two approaches: prediction of future states from planning with adaptivity to current situations from learning. However, a problem with is approach is that the two components should share a common representation of the information about the environment (e.g., states and actions). Previous work addresses this problem in the case where planning and learning are deﬁned over different state variables, by deﬁning a joint state space and a mapping between the two representations. In this paper, we present a method for integrating planning and reinforcement learning using a modular design where the two components can use their own representation formalism, without requiring an explicit mapping between them. More speciﬁcally, we introduce the concept of domain-independent reward machines, generated by a goal-oriented planning system and use them to drive a reinforcement learning agent to reach a goal state. More-over, we show how to automatically generate and use sub task decomposition to speed up the reinforcement learning process.
Modulo: Finding Convergence Failure Bugs in Distributed Systems with Divergence Resync Models

While there exist many consistency models for distributed systems, most of those models seek to provide the basic guarantee of convergence: given enough time and no further inputs, all replicas in the system should eventually converge to the same state. However, because of Convergence Failure Bugs (CFBs), many distributed systems do not provide even this basic guarantee. The violation of the convergence property can be crucial to safety-critical applications collectively working together with a shared distributed system. Indeed, many CFBs are reported as major issues by developers. Our key insight is that CFBs are caused by divergence, or differences between the state of replicas, and that a focused exploration of divergence states can reveal bugs in the convergence logic of real distributed systems while avoiding state explosion. Based on this insight, we have designed and implemented Modulo, the ﬁrst Model-Based Testing tool using Divergence Resync Models (DRMs) to systematically explore divergence and convergence in real distributed systems. Modulo uses DRMs to explore an abstract state machine of the system and derive schedules, the intermediate representation of test cases, which are then translated into test inputs and injected into systems under test (SUTs). We ran Modulo to check ZooKeeper, MongoDB, and Redis and found 11 bugs (including 6 previously unknown ones)
Sub-1V Bandgap Voltage Reference Circuit and Voltage to Current Converter

—This thesis explores the design steps of a Bandgap Voltage Reference (BGR) circuit and a voltage to current converter, implemented in one system. Both circuits are crucial blocks for high performance and reliable circuits, with a wide range of applications in many ﬁelds from analog to digital circuits. This work contemplates the design of the various sub-circuits which integrates both the BGR circuit and the voltage to current converter, including trimming sections, to improve the performance in all conditions that the system might be exposed to. The system was designed in a 180 nm conventional CMOS process technology. In the case of the BGR circuit, simulated results have shown temperature coefﬁcients as low as 20.8 ppm/°C over a temperature range of 165 °C (-40 °C to 125 °C). Additionally, the circuit displays a PSRR of -45.7 dB at low frequencies and -39 dB at 1 MHz. The converter circuit presents a process corners current variation of 3.2 %, for the same temperature interval. The system current consumption is 137.1 µ
Teaching Personalized Robot Navigation through Virtual Reality Demonstrations: A Learning Framework and User Study

—For the most comfortable, human-aware robot navigation, subjective user preferences need to be taken into account. This paper presents a novel reinforcement learning framework to train a personalized navigation controller along with an intuitive virtual reality demonstration interface. The conducted user study provides evidence that our personalized approach signiﬁcantly outperforms classical approaches with more comfortable human-robot experiences. We achieve these results using only a few demonstration trajectories from non-expert users, who predominantly appreciate the intuitive demonstration setup. As we show in the experiments, the learned controller generalizes well to states not covered in the demonstration data, while still reﬂecting user preferences during navigation. Finally, we transfer the navigation controller without loss in performance to a real robot.
Tzimiropoulos, G. (2015) A 3D scene analysis framework and descriptors for risk evaluation. In: 2015 International Conference on 3D Vision (3DV), 19-22

In this paper we evaluate the notion of scene analysis with regard to risk. We consider the problem of evaluating risk and potential hazards in an environment and providing a quantiﬁed risk score. A deﬁnition of risk is given incorporating two elements; Firstly scene stability, where Newtonian Physics are introduced into the scene analysis process, evaluating object stability within a scene. The effectiveness of which is demonstrated by conducting experiments on several scenes including a variety of stability levels. Secondly the analysis of the intrinsic risk related properties of an object, which is estimated using learning techniques and the utilisation of the 3D Voxel HOG descriptor, analysed against the state-of-the-art descriptors. Finally a new dataset is provided that is designed for scene analysis focusing on risk evaluation.
Movie Recommendation System Tuned Asymmetric Singular Value Decomposition

In recent years, the need for more accurate recommender systems to improve user interaction and provide more personalized services on eCommerce platforms such as Amazon and Netflix has increased globally. The motivation results from a desire to help users find an appropriate product that fits their tastes and meets various special needs, enhancing users’ satisfaction and loyalty. However, with the overload of vast amounts of customer data, recommender systems face challenges in processing data robustly and accurately. This proposal focuses on designing a movie recommendation sys-tem that takes into account both explicit and implicit ratings and performs well when new users are added to the original dataset. The base algorithm in my paper is Singular Value Decomposition (SVD), an applied matrix factorization method of the item-based collaborative filtering model. To solve scalability matters, reduce the expensive matrix factorization steps, and integrate implicit feed-back in the model, the tuning of Asymmetric SVD is expected to improve prediction accuracy.
Faster Yet Safer: Logging System Via Fixed-Key Blockcipher

System logs are crucial for forensic analysis, but to be useful, they need to be tamper-proof. To protect the logs, a number of secure logging systems have been proposed from both academia and the industry. Unfortunately, except for the recent KennyLoggings construction, all other logging systems are broken by an attack of Paccagnella et al. (CCS 2020). In this work, we build a secure logging system that improves KennyLoggings in several fronts: adoptability, security, and performance. Our key insight for performance gain is to use AES on a ﬁxed, known key. While this trick is widely used in secure distributed computing, this is the ﬁrst time it has found an application in the area of symmetric-key cryptography.
Linear Rank Intersection Types

Non-idempotent intersection types provide quantitative information about typed programs, and have been used to obtain time and space complexity measures. Intersection type systems characterize termination, so restrictions need to be made in order to make typability decidable. One such restriction consists in using a notion of finite rank for the idempotent intersection types. In this work, we define a new notion of rank for the non-idempotent intersection types. We then define a novel type system and a type inference algorithm for the lambda-calculus, using the new notion of rank 2. In the second part of this work, we extend the type system and the type inference algorithm to use the quantitative properties of the non-idempotent intersection types to infer quantitative information related to resource usage.
Modeling Network Contagion Via Interacting Finite Memory Pólya Urns

We construct a system of interacting finite memory Pólya urns to model contagion spread in a network. The urns, which are composed of red and black balls (representing degrees of infection and healthiness, respectively) interact in the sense that the probability at any time instant of drawing a red ball for a given urn not only depends on that urn’s ratio of red balls, but also on the ratio of red balls in the other urns of the network, hence accounting for the effect of spatial contagion. The urns have a finite memory, M, in the sense that reinforcing (black or red) balls added to each urn at time t are only kept in that urn for M future time instants (until time t + M). The resulting vector of all urn drawing variables forms an Mth order time-invariant irreducible and aperiodic Markov chain. We analytically examine the properties of the underlying Markov process and derive its asymptotic behaviour for the case of homogeneous system parameters. We further use mean-field approximation to obtain a class of approximating linear and nonlinear dynamical systems for the non-homogeneous case. Finally, we present simulations to assess the quality of these mean-field approximations.
Blocking Influence at Collective Level with Hard Constraints (Student Abstract)

Influence blocking maximization (IBM) is crucial in many critical real-world problems such as rumors prevention and epidemic containment. The existing work suffers from: (1) concentrating on uniform costs at the individual level, (2) mostly utilizing greedy approaches to approximate optimization, (3) lacking a proper graph representation for influence estimates. To address these issues, this research introduces a neural network model dubbed Neural Influence Blocking (\algo) for improved approximation and enhanced influence blocking effectiveness. The code is available at https://github.com/oates9895/NIB.
Empirical Analysis of the GitHub Actions ecosystem ⋆

The importance of open-source software (OSS) development has increased significantly throughout the last years, covering almost every application domain. Moreover, the usage of continuous integration and delivery (CI/CD) tools in software development has increased significantly. CI/CD tools aim to automate the build and delivery process with the goal of early error discovery and overall quality improvement. One of the most growing CI/CD tools is GitHub Actions, the integrated solution within GitHub. The aim of my PhD, under the supervision of Prof. Tom Mens is to explore the GitHub Actions ecosystem, by empirically analysing the use, impact, and evolution of GitHub Actions in collaborative OSS development.
Theory of Planned Behavior

Abstract Based on the theory of planned behavior (TPB), this study aimed to examine factors influencing therapist uptake of digital mental health interventions in Israel. Two hundred twenty-nine mental health professionals recruited through a convenience sample completed a background and demographic questionnaire, as well as the TPB-based electronic-therapy attitudes and process questionnaire. Regressions were used to examine the contribution of attitudes, perceived behavioral control, subjective norms, and behavioral intentions. Intention to use and use of digital interventions were significantly predicted by attitudes, subjective norms, and perceived behavioral control, as suggested by the TPB. The use of cognitive behavioral therapy (CBT) and higher perceived behavioral control increased the likelihood of using digital interventions. Having a positive attitude, having expectations/social pressure from one’s professional environment, and having a sense of control over the process were found to predict the intent to use, as well as the actual use of, digital interventions.
Efﬁcient seismic deghosting using convolutional neural networks

Ghost reﬂections deteriorate the quality of seismic data in towed-streamer acquisitions. Various acquisition and processing solutions have been proposed to remove ghost reﬂections from seismic data. A common issue with the proposed solutions is their limited ability to remove source-side ghosts because of the sparse source sampling in towed-streamer seismic acquisitions. Additionally, satisfactory receiver-side deghosting solutions require complementary measurements, e.g., particle motion data, for waveﬁeld separation. We propose a solution based on convolutional neural networks (CNNs) to remove source-side and receiver-side ghosts in the shot domain. The solution does not require complementary measurements, i.e. it can remove ghost re-ﬂections in conventional pressure data measured at a single reference surface. Our so-lution requires knowledge of the acquisition conﬁguration to create training data that replicates the ﬁeld acquisition geometry and requires the ocean ﬂoor bathymetry to be known. A CNN learns to map ghost-contaminated gathers to corresponding ghost-free gathers through an iterative training process. We show using a realistic synthetic example that the CNN-based deghosting operator can remove ghost reﬂections from previously unseen data. We demonstrate that the solution generalizes well to data generated using a velocity model intrinsically different from velocity models used to generate training data. Further, we show that our solution is robust to random noise, even though the training data are noise-free.
Continuous Biometric Identification on the Steering Wheel

For the great amount of time people spend inside them, cars have been evolving from mere means of transport to supersized gadgets, filled with top-notch software technology to increase comfort, functionality, safety, and security. Biometric recognition can be the key to automatic customisation of security, comfort, and driving settings, such as seat and mirror positions, GPS favourite locations, or speed monitoring, according to the driver’s identity. At a greater scale, it can also serve big companies in their fleet management and supervision tasks, continuously recognizing drivers to avoid fatigue, and apportion blame in case of reckless driving or accidents. This dissertation aimed to perform an extensive study on prior art and novel approaches in ECG-based recognition, with the final goal of formulating a effective and reliable continuous biometric system, able to work with highly noisy, off-the-person ECG signals acquired at the steering wheel. This required overcoming the noticeable noise corruption, including frequent periods of sensor saturation, achieved through the extensive comparison of several denoising methods, Rpeak detection algorithms, normalisation techniques, and outlier detection approaches. Likewise, various feature sets and recognition methods from the prior art were explored, in order to ensure the best results possible. Despite the evident challenges brought by such settings, it was found that a succession of fivesecond signal segmentation, denoising with Savitzky-Golay and a Moving Average Filter, heartbeat segmentation after Trahanias R-peak detection, and outlier removal based on cross-correlation clustering, was able to significantly clean the signal. Having relatively clean ensemble heartbeats, DCT features followed by SVM classification enabled the achievement of 2.66% EER and 91.82% IDR with driving signals, as well as 0.60% EER and 97.23% IDR with signals from the public UofTDB collection. The same method, under train settings constrained to the first thirty seconds of data, aided by a novel past score weighting procedure, was able to achieve 11.77% EER and 69.63% IDR with driving signals. These results prove that, with further improvements and the inclusion of template/model update techniques, it will be possible to develop a reliable continuous biometric system with ECG signals acquired on the steering wheel.
Introgression of Aegilops speltoides segements in Triticum aestivum and the effect of the gametocidal genes. Botany, (12).

• Background and Aims Bread wheat ( Triticum aestivum ) has been through a severe genetic bottleneck as a result of its evolution and domestication. It is therefore essential that new sources of genetic variation are generated and utilized. This study aimed to generate genome-wide introgressed segments from Aegilops speltoides . Introgressions generated from this research will be made available for phenotypic analysis. • Methods Aegilops speltoides was crossed as the male parent to T. aestivum ‘Paragon’. The interspecific hybrids were then backcrossed to Paragon. Introgressions were detected and characterized using the Affymetrix Axiom Array and genomic in situ hybridization (GISH). • Key Results Recombination in the gametes of the F 1 hybrids was at a level where it was possible to generate a genetic linkage map of Ae. speltoides . This was used to identify 294 wheat/ Ae. speltoides introgressions. Introgressions from all seven linkage groups of Ae. speltoides were found, including both large and small segments. Comparative analysis showed that overall macro-synteny is conserved between Ae. speltoides and T. aestivum , but that Ae. speltoides does not contain the 4A/5A/7B translocations present in wheat. Aegilops speltoides has been reported to carry gametocidal genes, i.e. genes that ensure their transmission through the gametes to the next generation. Transmission rates of the seven Ae. speltoides linkage groups introgressed into wheat varied. A 100 % transmission rate of linkage group 2 demonstrates the presence of the gametocidal genes on this chromosome. • Conclusions A high level of recombination occurs between the chromosomes of wheat and Ae. speltoides , leading to the generation of large numbers of introgressions with the potential for exploitation in breeding programmes. Due to the gametocidal genes, all germplasm developed will always contain a segment from Ae. speltoides linkage group 2S, in addition to an introgression from any other linkage group. of polymorphic SNPs between Ae. speltoides and hexaploid wheat based on the Affymetrix 35K array. A comparison is made between all calls (all markers showing polymorphism between wheat and Ae. speltoides) and Poly High Resolution (PHR) calls, which are codominant and polymorphic between wheat and Ae. speltoides with at least two examples of the minor allele
mizer: an R package for multispecies, trait-based and community size spectrum ecological modelling

Size spectrum ecological models are representations of a community of individuals which grow and change trophic level. A key emergent feature of these models is the size spectrum; the total abundance of all individuals that scales negatively with size. The models we focus on are designed to capture fish community dynamics useful for assessing the community impacts of fishing. We present mizer, an R package for implementing dynamic size spectrum ecological models of an entire aquatic community subject to fishing. Multiple fishing gears can be defined and fishing mortality can change through time making it possible to simulate a range of exploitation strategies and management options. mizer implements three versions of the size spectrum modelling framework: the community model, where individuals are only characterized by their size; the trait‐based model, where individuals are further characterized by their asymptotic size; and the multispecies model where additional trait differences are resolved. A range of plot, community indicator and summary methods are available to inspect the results of the simulations.