Miracle-dz/UniFAR-FTUs huggingface dataset:
First-Order Bayesian Regret Analysis of Thompson Sampling

We address online combinatorial optimization when the player has a prior over the adversary’s sequence of losses. In this setting, Russo and Van Roy proposed an information theoretic analysis of Thompson Sampling based on the information ratio, allowing for elegant proofs of Bayesian regret bounds. In this paper we introduce three novel ideas to this line of work. First we propose a new quantity, the scale-sensitive information ratio, which allows us to obtain more refined first-order regret bounds (i.e., bounds of the form <inline-formula> <tex-math notation="LaTeX">$O(\sqrt {L^{*}})$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$L^{*}$ </tex-math></inline-formula> is the loss of the best combinatorial action). Second we replace the entropy over combinatorial actions by a coordinate entropy, which allows us to obtain the first optimal worst-case bound for Thompson Sampling in the combinatorial setting. We additionally introduce a novel link between Bayesian agents and frequentist confidence intervals. Combining these ideas we show that the classical multi-armed bandit first-order regret bound <inline-formula> <tex-math notation="LaTeX">$ \widetilde {O}(\sqrt {d L^{*}})$ </tex-math></inline-formula> still holds true in the more challenging and more general semi-bandit scenario. This latter result improves the previous state of the art bound <inline-formula> <tex-math notation="LaTeX">$ \widetilde {O}(\sqrt {(d+m^{3})L^{*}})$ </tex-math></inline-formula> by Lykouris, Sridharan and Tardos. Moreover we sharpen these results with two technical ingredients. The first leverages a recent insight of Zimmert and Lattimore to replace Shannon entropy with more refined potential functions in the analysis. The second is a Thresholded Thompson Sampling algorithm, which slightly modifies the original algorithm by never playing low-probability actions. This thresholding results in fully <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>-independent regret bounds when <inline-formula> <tex-math notation="LaTeX">$L^{*}\leq \overline {L} ^{*}$ </tex-math></inline-formula> is almost surely upper-bounded, which we show does not hold for ordinary Thompson Sampling.
Validating Optimization with Uncertain Constraints

We consider optimization with uncertain or probabilistic constraints under the availability of limited data or Monte Carlo samples. In this situation, the obtained solutions are subject to statistical noises that affect both the feasibility and the objective performance. To guarantee feasibility, common approaches in data-driven optimization impose constraint reformulations that are "safe" enough to ensure solution feasibility with high confidence. Often times, selecting this safety margin relies on loose statistical estimates, in turn leading to overly conservative and suboptimal solutions. We propose a validation-based framework to balance the feasibility-optimality tradeoff more efficiently, by leveraging the typical low-dimensional structure of solution paths in these data-driven reformulations instead of estimates based on the whole decision space utilized by past approaches. We demonstrate how our approach can lead to a feasible solution with less conservative safety adjustment and confidence guarantees.
What Do Parents Value in Education? An Empirical Investigation of Parents&Apos; Revealed Preferences for Teachers

This paper examines revealed parent preferences for their children&apos;s education using a unique data set that includes the number of parent requests for individual elementary school teachers along with information on teacher attributes including principal reports of teacher characteristics that are typically unobservable. We find that, on average, parents strongly prefer teachers that principals describe as good at promoting student satisfaction and place relatively less value on a teacher&apos;s ability to raise standardized math or reading achievement. These aggregate effects, however, mask striking differences across family demographics. Families in higher poverty schools strongly value student achievement and are essentially indifferent to the principal&apos;s report of a teacher&apos;s ability to promote student satisfaction. The results are reversed for families in higher-income schools.
How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

Chronic stress leads to poor well-being, and it has effects on life quality and health. Society may have significant benefits from an automatic daily life stress detection system using unobtrusive wearable devices using physiological signals. However, the performance of these systems is not sufficiently accurate when they are used in unrestricted daily life compared to the systems tested in controlled real-life and laboratory conditions. To test our stress level detection system that preprocesses noisy physiological signals, extracts features, and applies machine learning classification techniques, we used a laboratory experiment and ecological momentary assessment based data collection with smartwatches in daily life. We investigated the effect of different labeling techniques and different training and test environments. In the laboratory environments, we had more controlled situations, and we could validate the perceived stress from self-reports. When machine learning models were trained in the laboratory instead of training them with the data coming from daily life, the accuracy of the system when tested in daily life improved significantly. The subjectivity effect coming from the self-reports in daily life could be eliminated. Our system obtained higher stress level detection accuracy results compared to most of the previous daily life studies.
Input parameter ranking for neural networks in a space weather regression problem

Geomagnetic storms are multi-day events characterised by significant perturbations to the magnetic field of the Earth, driven by solar activity. Numerous efforts have been undertaken to utilise in-situ measurements of the solar wind plasma to predict perturbations to the geomagnetic field measured on the ground. Typically, solar wind measurements are used as input parameters to a regression problem tasked with predicting a perturbation index such as the 1-minute cadence symmetricH (Sym-H) index. We re-visit this problem, with two important twists: (i) An adapted feedforward neural network topology is designed to enable the pairwise analysis of input parameter weights. This enables the ranking of input parameters in terms of importance to output accuracy, without the need to train numerous models. (ii) Geomagnetic storm phase information is incorporated as model inputs and shown to increase performance. This is motivated by the fact that different physical phenomena are at play during different phases of a geomagnetic storm.
An agent-based model framework for understanding the decisions of households and exploring bottom-up effects on housing sustainability

Housing plays a crucial role in the transition towards sustainability. Zero-energy or low-carbon housing solutions are key, but their success depends on their acceptance by the users. We propose a conceptual and an operational framework for understanding households’ residential choices as a base for an agent-based model, using the tenant as basic unit to populate it. We firstly conceptualise the decision-making process of tenants as a system. Secondly, we cluster the synthetic agents populating the model into typologies of dwellings and tenants. Thirdly, we display their actions in the action arena, and set-up the test runs. Lastly, we introduce the next steps to follow for the simulation of the bottom-up effects on housing sustainability.
Internal Marketing Practice and Implementation in Nordea Master ’ s thesis within Business Administration

Introduction:Based on a number of economical shifts it has become more and more important for companies to differentiate themselves from their competitors by effectively offer a high quality service. This has led to an increased need for internal marketing due to the high extent of human contact that is a reality when dealing with services. Internal marketing aims towards creating a service oriented workforce through recruiting and retaining appropriate employees. However, the concept needs research in how it is implemented due to the fact that there is no unified definition of the concept. Purpose: The purpose with this thesis is to analyse how internal marketing is implemented in practice in a Swedish bank organisation (Nordea), with focus on the recruitment and retention process. Method: To conduct this research a qualitative method has been chosen. The research is based on a case study of above mentioned organisation. This organisation has been chosen since the author had the possibility to get in contact with people in valuable positions to collect relevant information. Conclusions: Through the analysis it has been concluded that the term internal marketing is not used as such in the organisation. However, there are signs of different activities and ideas of the concept being implemented. As has been identified in the analysis, the notion that satisfied employees leads to satisfied customers seems understood in the organisation. Further it is identified that there is a close involvement from the marketing department in the recruitment process of the company. This involvement aims towards hiring people that can “live the brand”. When recruiting, the organisation primarily recruits from within. The retention process of the organisation is concerned with trying to create employee satisfaction which in turn will lead to the retention of appropriate employees and to satisfied customers, something the organisation calls “the good circle”. Magisteruppsats inom företagsekonomi Titel: Internmarknadsföring: Praktik och Implementering i Nordea. Författare: Linus Rydberg Handledare: Helen Anderson
Adoption of Internet Banking Services by Corporate Customers for Forex Transactions Based on the TRA Model

The digital banking system provides numerous advantages to customers, however, there is still a lack of trust among some corporate customers. Absence of face-to-face interaction makes it very impersonal. Thus, customers are more comfortable dealing with people in a physical bank setting that provides personalized services as opposed to mechanical interaction. Many customers still do not trust the online mode of service especially for money related transactions. Users who are not familiar with internet banking feel very uncomfortable as they have doubts regarding the accuracy of the transactions done online. The study looked to identify the factors inhibiting the corporate customers of FNB Industrial Branch from adopting internet banking technologies. The data was collected through closed ended questionnaires that were administered face-to-face within FNB Industrial Branch. The study focused on corporate customers and was administered to 132 respondents which represented an 88% response rate. The data labelling and cleaning were done using SPSS. Cronbach’s Alpha was used for measuring the total consistency between all items of the instrument and internal consistency among items for each dimension. After reliability analysis, it was established that the key factors influencing internet banking were: performance expectancy, control factors, social influence and behavioural intention. A simple regression analysis was conducted to test the research hypotheses following the criteria established by considering use of internet for forex transactions as the dependent variable. All of the independent variables were positive but only one was significant at the confidence level of P ≤ 0.05. Only social influence is said to have an effect on internet use for Forex transactions, therefore, based on the study, social influence would be a stronger factor leading to adoption of internet banking for Forex transactions.
NoMoATS: Towards Automatic Detection of Mobile Tracking

Abstract Today’s mobile apps employ third-party advertising and tracking (A&T) libraries, which may pose a threat to privacy. State-of-the-art detects and blocks outgoing A&T HTTP/S requests by using manually curated filter lists (e.g. EasyList), and recently, using machine learning approaches. The major bottleneck of both filter lists and classifiers is that they rely on experts and the community to inspect traffic and manually create filter list rules that can then be used to block traffic or label ground truth datasets. We propose NoMoATS – a system that removes this bottleneck by reducing the daunting task of manually creating filter rules, to the much easier and scalable task of labeling A&T libraries. Our system leverages stack trace analysis to automatically label which network requests are generated by A&T libraries. Using NoMoATS, we collect and label a new mobile traffic dataset. We use this dataset to train decision tree classifiers, which can be applied in real-time on the mobile device and achieve an average F-score of 93%. We show that both our automatic labeling and our classifiers discover thousands of requests destined to hundreds of different hosts, previously undetected by popular filter lists. To the best of our knowledge, our system is the first to (1) automatically label which mobile network requests are engaged in A&T, while requiring to only manually label libraries to their purpose and (2) apply on-device machine learning classifiers that operate at the granularity of URLs, can inspect connections across all apps, and detect not only ads, but also tracking.
Agreement between Inertia and Optical Based Motion Capture during the VU-Return-to-Play- Field-Test

The validity of an inertial sensor-based motion capture system (IMC) has not been examined within the demands of a sports-specific field movement test. This study examined the validity of an IMC during a field test (VU®) by comparing it to an optical marker-based motion capture system (MMC). Expected accuracy and precision benchmarks were computed by comparing the outcomes of a linear and functional joint fitting model within the MMC. The kinematics from the IMC in sagittal plane demonstrated correlations (r2) between 0.76 and 0.98 with root mean square differences (RMSD) < 5°, only the knee bias was within the benchmark. In the frontal plane, r2 ranged between 0.13 and 0.80 with RMSD < 10°, while the knee and hip bias was within the benchmark. For the transversal plane, r2 ranged 0.11 to 0.93 with RMSD < 7°, while the ankle, knee and hip bias remained within the benchmark. The findings indicate that ankle kinematics are not interchangeable with MMC, that hip flexion and pelvis tilt higher in IMC than MMC, while other measures are comparable to MMC. Higher pelvis tilt/hip flexion in the IMC can be explained by a one sensor tilt estimation, while ankle kinematics demonstrated a considerable level of disagreement, which is likely due to four reasons: A one sensor estimation, sensor/marker attachment, movement artefacts of shoe sole and the ankle model used.
Finite element analysis of a composite piston for a diesel aircraft engine

The article presents calculations of thermal and mechanical loads of the piston, consisting of two parts: steel and aluminum. The calculations were made using FEM in the Abaqus software. The piston is characterized by a split construction and was equipped with a cooling oil channel. The piston will be used in an aircraft diesel engine characterized by opposite piston movement. The presented geometry of the piston is the next of the ones being developed earlier and contains preliminary assumptions as to the size and main geometrical dimensions. The thermal boundary conditions of the simulation tests assumed defined areas of heat reception surface and heating of the piston by defining a temperature map on its crown. The results of these studies were presented in the form of temperature distribution and heat flux on the surface of the tested element. The strength boundary conditions assumed a mechanical load in the form of pressure resulting from the pressure in the combustion chamber applied to the piston crown surface and the opposite pressure defined on the support at the surface of contact between the piston and the piston pin. The results of these tests were presented in the form of stress distribution on the surface of the tested element. As a result of the analyses carried out, the results constituting the basis for further modernization of the piston geometry were obtained.
Bayesian OMP-based Joint Active User Detection and Channel Estimation for Massive Machine Type Communications

In this paper, we propose a novel method to jointly estimate the activities and channel coefficients of devices in massive machine-type communications (mMTC) systems. We formulate a maximum a posteriori probability (MAP) estimation problem by exploiting the statistical prior information including the channel distribution and device activity probability. The formulated MAP problem is solved by a greedy algorithm, named Bayesian orthogonal matching pursuit (Bayesian OMP, BOMP). We observe that the proposed method improves the activity detection and channel estimation performance compared to the conventional methods. keywords: Massive machine-type communications, compressed sensing, bayesian orthogonal matching pursuit student number: 2016-20953
Application of an Optimized Expert System in Development of Simplified Turbomachinery Modelling

Simulating the ingestion of non-uniform in ow to a fan or compressor requires enormous computational resources if the full details of the ow in the blade rows being studied is to be resolved, since full-wheel unsteady computations are required. A simpli ed modelling approach exists as an alternative computational option, which is the use of volumetric source terms (body forces) in place of the physical blades. Typically, body force models are manually calibrated with reference to single passage simulation results, and demands signi cant user experience and expertise. The objective of this thesis is to eliminate the need for experience and expertise during model calibration as much as is practical by employing an automated expert system. The modelling approach employed in this work is the combination of an existing turning force model, and an adaptation of an existing viscous force model. The automated system is implemented into Matlab and makes use of Ansys CFX as the ow solver. User input is required to initialize the system but the procedure then runs through to convergence of the nal, calibrated model. Viscous force model coe cients that are traditionally found through an iterative procedure, are instead subjected to a Nelder-Mead optimization process. The machine studied as an example of the application of the automated technique is the NASA stage 67 transonic compressor. At peak e ciency, the isentropic rotor and stage e ciency, and the rotor work coe cient are matched within 1% of their single passage counterparts, a result that is on par with a manually generated body force model. A key nding in this thesis is that the stage e ciency is not the optimal parameter used for calibration of the stator's viscous force model. Despite this nding, the model produced performs su ciently at o -design conditions not nearing choke. Across the speedline simulated, the model predicts the rotor total temperature ratio, total pressure ratio, and the stage total pressure ratio to within 1.3% of the single passage result. The computational time required for the calibration of the model produced from this work is 23 core-days. Although this computational cost remains relatively high, the removal of nearly all required user experience is achieved.
Development of Biosignal Telemonitoring System Based on HL7 and MFER Standard.

Objective: We developed a biosignal telemonitoring system which is based on HL7 and MFER standard. Methods: For the communication of waveform data (ECG, EEG etc.) we adopted MFER(Medical waveform description Format Encoding Rules) standard and HL7(Health Level 7). MFER is a standard for encoding waveform biosignal such as ECG, EEG and so on. HL7 is a standard for electronic data communication between two different information systems. Results: The telemornitoring system consists of an HL7 interface gateway and a central repository. The HL7 interface gateway has three modules of an MFER analyzer, an HL7 message generator, and a file sender. The central repository is a central database combined with an HL7 archiver. Conclusion: Through this study we might conclude that the proposed system can be a promising model for telemonitoring system in healthcare environment.
Effects of Seat Cushion Material on Center of Pressure and Movement Trajectory during a Reaching Task

This study investigated the effects of seat cushion material during a reaching task. Ten healthy adults and three individuals with cerebral palsy participated. All subjects performed the reaching task on two urethane cushions and one silicone cushion. Anterior/posterior and medial/lateral displacement of the center of pressure and straight rate were assessed. In addition, the movement trajectory of the hand was recorded. No differences among seat cushions were observed in healthy adults. However, all of individuals with cerebral palsy were able to perform the task more efficiency while seated on the silicone cushion than the others. These findings suggest that seat cushion design may improve movement in individuals with cerebral palsy who have postural control disabilities.
ssc: An R Package for Semi-Supervised Classification

Semi-supervised classification has become a popular area of machine learning, where both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. We present the R package ssc that implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented in the ssc package can be applied to classification problems in several domains by the specification of a suitable learning scheme. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.
ScholarWorks @ UTRGV ScholarWorks @ UTRGV Integer Versus Fractional Order SEIR Deterministic and Stochastic Integer Versus Fractional Order SEIR Deterministic and Stochastic Models of Measles Models of Measles

: In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-ﬁeld approximations of integer and fractional stochastic processes, we introduce the fractional differential equations (FDEs) as approximations of some type of fractional nonlinear birth and death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we ﬁt both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While ordinary differential equations (ODEs) are commonly used to model epidemics, FDEs are more ﬂexible in ﬁtting empirical data and theoretically offer improved model predictions. The question arises whether, in practice, the beneﬁts of using FDEs over ODEs outweigh the added computational complexities. While important differences in transient dynamics were observed, the FDE only outperformed the ODE in one of out three data sets. In general, FDE modeling approaches may be worth it in situations with large reﬁned data sets and good numerical algorithms.