Miracle-dz/UniFAR-FTUs huggingface dataset:
Artiﬁcial Player for the Competitive Game Geometry Friends

This work has the goal of improving the artiﬁcial intelligence of a circle-shaped, whose objective is to collect all diamonds available on each level of the platform game called Geometry Friends. This game is part of the Artiﬁcial Intelligence competitions of the IEEE CIG Conference, where participants submit their agents to be tested in both single-player and cooperation levels. The solution for the implementation of these agents is based on a reinforcement learning approach (SARSA algorithm) and divides the problem of solving the game in three sub-problems: solving one platform, deciding the next platform and moving from one platform to another. The ﬁrst and third sub-problems will be focused throughout this dissertation, where the agent will be tested in a set of simple but very diﬀerent and symmetrical levels that focus typical challenges that the player faces in a normal level.
MultiViz: Data Visualization of Patient Records

We have developed an interactive visualization tool to improve task efficiency in medical patient record analysis. Large quantities of heterogeneous data need to be processed in order to infer diagnosis and treatments for a given patient. Visualizing this data allows easier creation of medical decisions, by making explicit relevant properties of observed attributes. We have conducted our work in collaboration with Egas Moniz Clinical University (EMCU), developing our tool iteratively with feedback from medical professionals. We attempted to improve discovery and exploration of previously unknown patterns in a given dataset, assisting the university in scaling up their current data system to meet medical professional needs. We discuss the current state of the art in medical visualizations, how we gathered tasks to be addressed by our visualization tool, the architecture of our solution, results from user testing, and suggestions for future work.
Fine-Tuning HVAC Operation Through RFID and User Feedback

Heating, Ventilating and Air-Conditioning (HVAC) systems account for about 60% of the total energy spent in buildings. A large share of these spendings regards incorrect adjustment of temperature setpoint, which remains constant independently of user occupancy and thermal comfort. More-over, constant temperature setpoints might induce over or under cooling which leads to user thermal discomfort. This thesis describes a collaborative application where HVAC temperature setpoints are adjusted based on user occupancy providing occupants a platform where they can vote for a diﬀerent temperature setpoint, while real-time comfort and energy savings values are available for feedback to users. The developed platform elicits user behaviour transformation leading occupants into an active role of adjusting the temperature setpoint, accordingly to roughly comfort preferences and prospective feedback, improving both thermal comfort and energy eﬃciency. The results show that it is technically possible to develop collaborative platforms similar to the one that was implemented, but the subjectivity of this case study leads to substantially diﬀerent results for the same experiment, when diﬀerent users are involved. One can conclude that the use of these platforms only ﬁt for scenarios where users are able reach a consensus on the subject of the case in analysis.
A census of dense cores in the Taurus L1495 cloud from the Herschel Gould Belt Survey

We present a catalogue of dense cores in a ∼4° × 2° field of the Taurus star-forming region, inclusive of the L1495 cloud, derived from Herschel SPIRE and PACS observations in the 70 μm, 160 μm, 250 μm, 350 μm, and 500 μm continuum bands. Estimates of mean dust temperature and total mass are derived using modified blackbody fits to the spectral energy distributions. We detect 525 starless cores of which ∼10–20 per cent are gravitationally bound and therefore presumably prestellar. Our census of unbound objects is ∼85 per cent complete for M > 0.015 M⊙ in low-density regions (AV ≲ 5 mag), while the bound (prestellar) subset is ∼85 per cent complete for M > 0.1 M⊙ overall. The prestellar core mass function (CMF) is consistent with lognormal form, resembling the stellar system initial mass function, as has been reported previously. All of the inferred prestellar cores lie on filamentary structures whose column densities exceed the expected threshold for filamentary collapse, in agreement with previous reports. Unlike the prestellar CMF, the unbound starless CMF is not lognormal, but instead is consistent with a power-law form below 0.3 M⊙ and shows no evidence for a low-mass turnover. It resembles previously reported mass distributions for CO clumps at low masses (M ≲ 0.3 M⊙). The volume density PDF, however, is accurately lognormal except at high densities. It is consistent with the effects of self-gravity on magnetized supersonic turbulence. The only significant deviation from lognormality is a high-density tail which can be attributed unambiguously to prestellar cores.
Variability Analysis in Ginger (Zingiber officinale Rosc.) Somaclones Using ISSR Marker

Ginger (Zingiber officinale Rosc.), an important spice crop grown in India, is much valued for its flavour and medicinal properties. Crop improvement through selection and hybridization are not effective in ginger due to lack of variability and absence of natural seed set. The earlier crop improvement programmes were hence focussed on mutation breeding using  rays and ethyl methyl sulfonate (EMS). The mutants thus isolated were low yielders and the effect of mutagen treatment vanished in subsequent generations [1, 2, 3]. Hence investigations were made to induce variability in ginger through biotechnological tools like in vitro pollination and exploitation of somaclonal variation. Currently, molecular marker techniques are widely employed to detect and assess somaclonal variation in several crops as they are stable, detectable in all tissues and are not confounded by environment, pleiotropic and epistatic effects. Inter Simple Sequence Repeats (ISSR) markers can be used to assess the genetic stability of micropropogated plants like ginger [4, 5]. In addition, Inter Simple Sequence Repeats are considered useful in gene mapping studies [6, 7,8], diversity analysis in many crops including ginger [9] and somaclonal variation [10]. The variability analysis in the thirteen groups of ginger somaclones (180 Nos.) of two cultivars Maran and Rio-de-Janeiro using ISSR marker system was attempted in the present study.
Making Predictions with Textual Contents Experiments with Portuguese Documents from Three Distinct Domains

Forecasting real-world quantities, with basis on information from textual descriptions, has recently attracted signiﬁcant interest as a research problem, although previous studies have focused on applications involving only the English language. This paper presents an experimental study on the subject of making predictions with textual contents written in Portuguese, using documents from three distinct domains. We speciﬁcally report on experiments using diﬀerent types of regression models, using state-of-the-art feature weighting schemes, and using features derived from cluster-based word representations. Our experiments show that prediction models using the textual information achieve better results than simple baselines such as the average value, and that richer document representations (i.e., using Brown clusters and the Delta-TF-IDF feature weighting scheme) may result in slight performance improvements.
Demand Forecasting for Alcoholic Beverage Distribution

Forecasting demand is one of the biggest challenges in any business, and the ability to make such predictions is an invaluable re-source to a company. While diﬃcult, predicting demand for products should be increasingly accessible due to the volume of data collected in businesses and the continuing advancements of machine learning models. This paper presents forecasting models for two vodka products for an alcoholic beverage distributing company located in the United States with the purpose of improving the company’s ability to forecast demand for those products. The results contain exploratory data analysis to determine the most important variables impacting demand, which are time of year and customer. For each of the two products, models were built to predict demand for three major customers. For each product/customer combination, this paper compares time series and deep learning models to a naive model to see if the prediction accuracy can be improved. For ﬁve out of six products, the time series models reduced error by 2.5– 66.7% compared to the naive models. Also, for one product, a hybrid CNN model developed for this paper outperformed the time series models by 3–10% and reduced error by 49% compared to the naive models.
Ready from Day One? The Relationship Between Length of Pre-Service Teacher Field Residency and Teacher Efficacy

The study examines the perceptions of pre-service teachers’ sense of efficacy in a teacher preparation program to determine whether a relationship exists between teacher efficacy and the amount of time spent in field residency. The paper first examines the existing literature on efficacy and current field residency practices in teacher preparation programs. Following the literature will be an overview of the methodology and results. The findings indicate a significant correlation between length of time in field residency experiences and the pre-service teachers’ sense of General Teaching Efficacy.
Qualia structures and their impact on the concrete noun categorization task

Automatic acquisition of qualia structures is one of the directions in information extraction that has received a great attention lately. We consider such information as a possible input for the word-space models and investigate its impact on the categorization task. We show that the results of the categorization are mostly influenced by the formal role while the other roles have not contributed discriminative features for this task. The best results on 3-way clustering are achieved by using the formal role alone (entropy 0.00, purity 1.00), the best performance on 6-way clustering is yielded by a combination of the formal and the agentive roles (entropy 0.09, purity 0.91).
The negative way to sentience

While the materialist paradigm is credited for the incredible success of science in describing the world, to some scientists and philosophers there seems to be something about subjective experience that is left out, in an apparently irreconcilable way. I show that indeed a scientific description of reality faces a serious limitation, which explains this position. On the other hand, to remain in the realm of science, I explore the problem of sentient experience in an indirect way, through its possible physical correlates. This can only be done in a negative way, which consists in the falsification of various hypotheses and the derivation of no-go results. The general approach I use here is based on simple mathematical proofs about dynamical systems, which I then particularize to several types of physical theories and interpretations of quantum mechanics. Despite choosing this scientifically-prudent approach, it turns out that various possibilities to consider sentience as fundamental make empirical predictions, ranging from some that can only be verified on a subjective basis to some about the physical correlates of sentience, which are independently falsifiable by objective means.
Plant Growth Regulators and Fungicides alters Growth and Biochemical contents in Mentha piperita Linn

The effect of different plant growth regulators (PGR) and fungicide treatments on the growth characteristics of Mentha piperita Linn. (pippermint, Family: Lamiaceae) was investigated in the present study. The PGR used were gibberellic acid (GA ), Abscisic acid (ABA) and fungicides used were Difenoconazole (DIZ) by soil drenching on 35, 50, 65 and 80 days after planting (DAP). The plants were taken randomly on 45, 60, 75 and 90 DAP and used for estimating the growth and biochemical changes. DIZ and ABA treatments increased the fresh weight, dry weight, root growth, total chlorophyll, protein and amino acid content, while it decreased the stem length. Among the treatments GA3 enhanced the fresh and dry weights and stem length increased the larger extent when compared with control.
Dislocation after hip fracture related arthroplasty

Femoral neck fracture is a common type of hip fracture. Arthroplasty is a widely accepted treatment of a displaced femoral neck fracture in elderly patients. Dislocation is a major complication and together with infection the most frequent reasons for revision surgery. Paper I is an observational cohort study, based on cross-matching data from the Swedish Hip Arthroplasty Register, the National Patient Register and Statistics Sweden. The aim of the study was to find the “true” dislocation rate of fracture-related total hip arthroplasties (THA) and to study factors influencing the risk of dislocation. We found the dislocation rate for THA to be 8.4% (posterior approach 13.4%, direct lateral approach 4.8%). Paper II is an observational cohort study, with the same design as Paper I. Here, the aim of the study was to find the “true” dislocation rate of fracture-related hemiarthroplasties (HA) and to study factors influencing the risk of dislocation. Dislocation rate for HA was 4.8% (posterior approach 7.2 %, direct lateral approach 2.7 %). Paper III is a cluster-randomised study of 394 patients. The aim was to study the need of rehabilitation precautions and mandatory assistive equipment to prevent dislocation when direct lateral approach is used for hemiarthroplasty after hip fracture. The results show no association with the risk of dislocation. Paper IV is an observational cohort study of 9040 patients based on data from the Nordic Arthroplasty Register Association. The aim was to evaluate both overall revision risk and specific revision causes (including dislocation) of the dual mobility acetabular cup (DMC), compared to conventional THA. The use of DMC as primary treatment for hip fracture was associated with a lower risk of revision in general and due to dislocation in particular. In conclusion we found the total dislocation rate after THA and HA as treatment of hip fracture to be high. Posterior approach is the most important risk factor for dislocation. Male gender and severe comorbidity are associated with an increased risk of dislocation after THA. Dementia is associated with an increased risk of dislocation after HA. Precautions do not seem to be needed to prevent dislocation after HA in hip fracture patients, if direct lateral approach is used. Finally, DMC after hip fracture is associated with a lower overall risk of revision and due to dislocation in particular, compared with conventional THA.
See through smoke: robust indoor mapping with low-cost mmWave radar

This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response. A unique feature of milliMap is that it only leverages a low-cost, off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy comparable to lidar, as well as providing semantic annotations of objects on the map. milliMap makes two key technical contributions. First, it autonomously overcomes the sparsity and multi-path noise of mmWave signals by combining cross-modal supervision from a co-located lidar during training and the strong geometric priors of indoor spaces. Second, it takes the spectral response of mmWave reflections as features to robustly identify different types of objects e.g. doors, walls etc. Extensive experiments in different indoor environments show that milliMap can achieve a map reconstruction error less than 0.2m and classify key semantics with an accuracy of ~ 90%, whilst operating through dense smoke.
Trustversion: A Secrecy-protected Version Control System

Version Control Systems (VCSes) are widely used by software developers to store their software projects in remote locations and to maintain a record of all modifications in their files. Considering that software projects may constitute valuable private data for developers, having such projects stored in remote servers can raise security concerns as the servers may not be trusted. Currently, VCSes, in particular Subversion, do not provide data confidentiality in the face of compromised servers. In this project, we designed a system to protect the data called Trustversion, which provides a solution for performing SVN operations while keeping the developers’ files encrypted on the Subversion repository. This solution protects the user’s private data, even if they use online SVN repository services, such as SourceForge, hosted in remote servers that may not be trusted. Trustversion also ensures low storage usage overhead by taking advantage of the Subversion mechanisms for storing data efficiently.
Credibility Improves Topical Blog Post Retrieval

Topical blog post retrieval is the task of ranking blog posts with respect to their relevance for a given topic. To improve topical blog post retrieval we incorporate textual credibility indicators in the retrieval process. We consider two groups of indicators: post level (determined using information about individual blog posts only) and blog level (determined using information from the underlying blogs). We describe how to estimate these indicators and how to integrate them into a retrieval approach based on language models. Experiments on the TREC Blog track test set show that both groups of credibility indicators significantly improve retrieval effectiveness; the best performance is achieved when combining them.
Implementation of Proposed Despeckling Algorithm in Spatial Domain

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract — Mostly the images captured through coherence illumination are formed with higher level of speckle noise. The success ratio of segmentation after the preprocessing of the image that involves denoising depends on the extent of the removal of noise from the image. In the preprocessing stage, the noise present in the medical image has to be removed while preserving the edge information and other structural details of the image. This research is focused on design of algorithms for speckle denoising of Ultra Sound images and Optical Coherence Tomography images in spatial domain. Standard speckle filters in spatial domain were analyzed and compared with the proposed method. Results obtained proved that the proposed method performed better than the existing spatial domain filters in denoising and preserving the edge details.
Elastic Named Data Network (NDN) for data centric application in cloud environments

Research projects such as ENVRI-FAIR both generate and consume large amounts of data. The use of persistent identiﬁers along the vision of a Global Digital Object Cloud in naming makes managing and locating the data much more palatable. These structured and persistent names along with the large amounts of data they represent aligns closely with the data model of Named Data Networking (NDN), a Future Internet architecture that makes data objects ﬁrst class citizens of the network, enabling among other things, widespread use of caching to reduce both bandwidth and latency. While the design of NDN enables content distribution in a more eﬃcient manner, operating such a network is still largely a manual process, especially in regards to scaling cope with varying demand levels. This research explores the possible ways for NDN networks adapt to elastic demand and proposes a load balanced conﬁguration to eﬃciently scale NDN nodes.
What are the expectations of Dutch young adults in terms of behaviour and appearance of care robots for the elderly? SUBMITTED IN PARTIAL FULFILLMENT FOR THE DEGREE OF MASTER OF SCIENCE

The following paper describes results of a qualitative study performed at the University of Amsterdam. Previous research has suggested that care-bots for elderly might be a solution to the increasing number of older adults. It has been implied that the acceptance of such robots might vary across different countries as cultural differences influence human attitudes towards robots. In the context of the Dutch culture, companion robots have been proven to be the most likely to get accepted. However, it was noted that user expectations and issues, for instance moral concerns, should be taken into account when designing such robots. In this study, expectations of Dutch young adults towards companion care robots have been investigated. The results show that for Dutch young adults utility is the most important value, that outweighs even ethical issues. The group is driven by pragmatism and expects robot companions to address problems of the elderly and the health-care system. Dutch young adults expect a robot practical in terms of both appearance and behaviour. A robot proposed could be described as a assistive social companion.
Sodium-Lithium Countertransport activity in first degree families of diabetics, susceptible persons for diabetes and diabetic patients; a possible biomarker for prognosis of diabetes

Sodium-lithium countertransport (SLC) is a membrane ion transport system. SLC activity is elevated in many metabolic disorders and genetic epidemiologic studies suggest that 50-80 % of SLC activity can be explained by inheritance. The aim of this study was to investigate the correlation between SLC activity and family history of diabetes. Eighty subjects in four equal experimental groups were used for this study. They were classified as diabetic patients, first degree families, susceptible persons and a control group. SLC activity was measured in their red blood cells. Preliminary experiments showed that plasma glucose as well as insulin levels were significantly higher in diabetics compared with other experimental groups. This group also showed higher activity of SLC. The activity of SLC in first degree relatives of diabetic patients was much higher than control subjects and diabetic patients. This study showed that the activity of SLC is changed in diabetes. It also indicated that their first degree relatives, even before the onset of the disease, had SLC activity higher than controls. This result is helpful in the prediction of diabetes and paves the way for the introduction of new strategies for handling the outcome of this life threatening disorder.
CIBC Working Paper Series

Rising costs of and returns to college have led to sizeable increases in the demand for student loans in many countries. In the U.S., student loan default rates have also risen for recent cohorts as labor market uncertainty and debt levels have increased. We discuss these trends as well as recent evidence on the extent to which students are able to obtain enough credit for college and the extent to which they are able to repay their student debts after. We then discuss optimal student credit arrangements that balance three important objectives: (i) providing credit for students to access college and finance consumption while in school, (ii) providing insurance against uncertain adverse schooling or post-school labor market outcomes in the form of income-contingent repayments, and (iii) providing incentives for student borrowers to honor their loan obligations (in expectation) when information and commitment frictions are present. Specifically, we develop a two-period educational investment model with uncertainty and show how student loan contracts can be designed to optimally address incentive problems related to moral hazard, costly income verification, and limited commitment by the borrower. We also survey other research related to the optimal design of student loan contracts in imperfect markets. Finally, we provide practical policy guidance for re-designing student loan programs to more efficiently provide insurance while addressing information and commitment frictions in the market.