Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Neural Network-based Voting Assistant

Voting Advice Applications (VAA) have become an integral part of today’s elections and voting procedures. They seemingly have not evolved a lot since their rise to popularity. Smartvote, a popular VAA in Switzerland, provided us with a large amount of data. With this data, from the Swiss national council elections of 2019, we push for innovation in the field of VAAs. Despite the large amounts of data available, machine learning has not yet established itself in the area of voting advice. In this thesis, we explore the possibilities given such data. In order to achieve our goals, we apply natural language processing techniques to solve both existing and new tasks. A common task is to recommend to voters the party with which they have the most overlapping political opinions. This is our starting task to ensure our understanding and the functionality of NLP methods for this data set. Most interestingly, we tackle tasks that could push democracy as well as VAAs forward, given the arising opportunities. We are able to finetune a Transformer based model, called Longformer[1], to predict answers for questions with very high testing accuracy, if the question is seen during training. The significantly more difficult task of predicting answers to questions unknown at the time of training also shows very promising results. We find the latter task to be very dependent on the chosen input context as well as the questions to be predicted. Therefore, the task outlines limitations of the data we use. Thus, encouraging the application of a model like ours for design choices when planning a questionnaire for voting advice. Our findings pave the way for new exiting possibilities in the domain of politics and advice systems, as we present a model that is able to learn political contexts and profiles, allowing it to predict future opinions.
A Review Paper on SQL Injection and Cross Site Scripting Vulnerabilities An enhance Android based application for mobile authentication security

—As of late, web remains the favored stage for clients to do their business exercises. The movement of utilizations to web has been quick extending from applications like E-trade, Public gathering, E-administration, E-saving money, Shopping Portals or some other applications running on the web. Web Applications have expanded its use on account of simple availability to various clients around the globe. Be that as it may, as the utilization of the web has expanded, it has likewise given a bothersome or dull side to the use of html. Cross-webpage scripting (XSS) assaults keep on remaining the highest risk to web applications, databases and sites far and wide for a lot of time now. A study of around 15 million digital assaults in the second from last quarter of 2012 has uncovered that the greater part of these assaults are XSS based. In spite of the fact that assaults like SQL Injection, CSRF and Phishing are likewise normal, XSS still remains the favored procedure for programmers to complete malevolent exercises on web. This paper examines about XSS assaults, their operation and distinctive classifications of XSS assaults. The paper additionally features the alleviation situation and strategies feasible for anticipation. Data enters a Web application through an unauthorized source, most much of the time a web ask. The information is incorporated into dynamic substance that is sent to a web client without being approved for malignant substance. The malignant substance sent to the web program regularly appears as a fragment of JavaScript, however may likewise incorporate HTML, Flash, or some other sort of code that the program may execute. The assortment of assaults in view of XSS is practically boundless, however they regularly incorporate transmitting private information, similar to treats or other session data, to the aggressor, diverting the casualty to web content controlled by the assailant, or performing different vindictive operations on the client's machine under the pretense of the powerless.
Unintended Consequences of Sanitation: Negative Externalities on Water Quality and Health in India

Developing countries have increased sanitation investment to reduce the transmission of diseases and diarrheal death. Despite these positive health effects, improved sanitation can cause negative externalities on ambient water quality and health due to poor treatment of fecal sludge generated from latrines. I estimate these negative externalities in the context of India’s national large-scale sanitation policy. By exploiting an exogenous variation in soil infiltration rates and differential increase in latrine coverage across districts, I document the negative externalities on fecal contamination of rivers and diarrheal infant mortality. These externalities were mitigated by the adequate implementation of a complementary policy, i.e., fecal sludge management policy. Back-of-the-envelope calculations show that this policy increased fecal contamination by 70%, and the magnitude of the reduction in diarrheal mortality was one-third smaller in areas with lower sewage treatment plant capacities. JEL: I15, O13, Q53, Q56
CD15 and CD15s in the Anticancer Therapy

CD15 (Lewis X/Lex) is a fucosyl (3-fucosly-N-acetyl-lactosamine) moiety found on membrane proteins of various cancer cells. These include renal cancer, prostate and bladder cancers, acute leukemias, hepatocellular carcinoma, breast cancer and melanoma. The antigen plays an especially significant role in renal cell carcinoma. Its high expression serves as a good prognostic marker for patients and high hopes are related to its use in the immunotherapy of the tumor. The biological role of CD15 is the interaction with E-, Land P-selectins (adhesion molecules) and allowing for the adhesion with the endothelial cells. In this way, cancer cells start to interact with the endothelium of blood vessels and consequently move out from the blood flow to the surrounding tissues. The blockage of antigen’s function results in reduced metastatic potential. Moreover, the molecule may be a therapeutic target against cancer in monoclonal antibodies-based therapies. CD15s is a sialyl derivative of CD15, that possess its own unique characteristics. Unlike high expression of CD15, which is a prognostic factor in Hodgkin lymphoma, CD15s relate to poor prognosis for the patients. Due to the high abundance in cancer cells, CD15 is considered as a marker of Cancer Stem Cells. This review presents a comprehensive description of the role of CD15 and CD15s in cancer development and metastasis and overviews the clinical applications of the anti-CD15 therapy.
An Efficient Data Compressing Method of GPS Position Data Transmission by Mobile Short Message

It is necessary to transmit combat vehicle's position data to management center by mobile short message in the information management system of armored forces.Due to the volume limit of the short message packet,each message carries limited data,so it's difficult to redraw the trail of the vehicle by the data without high cost and frequent transmission.To transmit sufficient data for the vehicle's trail,a vehicle's position data compression transmission method is proposed in this paper.This method can raise the efficiency by more than 30 times with little cost and is used in the vehicle management system.
Promoting Feature Awareness by Leveraging Collaborators’ Usage Habits in Collaborative Editors

Users often rely on their collaborators to find relevant application features by observing them “over the shoulder” (OTS), usually in a synchronous co-located setting. However, as remote work set- tings have become more common, users can no longer rely on such in-person interaction with collaborators. Therefore, we investigate designs that help the user become aware of relevant features based on collaborators’ feature usage habits. We created five design concepts as video prototypes which varied in five design dimensions: number of active collaborators, number of shared documents, specificity of comparison, user involvement, and goal of the feature awareness. Interviews (N=18) probing the design concepts indicate that collaborator-based feature awareness would be valuable for discovering novel features and producing a consistent style across the shared document, but some users may feel micromanaged or self-conscious. We conclude by reflecting on and expanding our design space and discussing future design directions supporting remote OTS learning.
Non-parametric Uncertainty Assessment in Deep-Learning Based Affine Image Registration

Affine image registration plays a key role in diagnosis, surgical planning and in data-processing pipelines for research as both an essential initialization for subsequent non-rigid registration or as an independent step. Uncertainty quantification in deep learning (DL) based image registration models is critical for determining confidence intervals required for surgical guidance, and for reliable assessment of differences be-tween the registered images. We introduce AIR-SGLD - a non-parametric fully Bayesian framework for Affine image registration. We use Stochastic gradient Langevin dynamics (SGLD) during the training phase to characterize the posterior distribution of the network weights. We demonstrated the added-value of AIR-SGLD on the brain MRI (MGH10) dataset in comparison to the baseline AIR DL-based Affine image registration framework using 300 pairs of images generated from the MGH10 dataset. Our experiments show that AIR-SGLD outperforms AIR by means of cross-correlation between the images (0 . 91 vs. 0 . 87, p < 0 . 01). Further, AIR-SGLD provides an estimate of the registration uncertainty that correlates with both registration error (Pearson correlation coefficient of R = 0 . 769) and the presence of out-of-distribution data ( R = 0 . 796). AIR-SGLD has the potential to provide reliable and more accurate registration for clinical diagnosis, surgical planning, and automatic data processing pipelines.
Modeling the synchronous permanent magnet generator with RLC load

This paper is concerned with the modeling of the three-phase permanent magnet synchronous generator (PMSG) and its load in the dq0 reference system. The generator was considered to be directly connected to a balanced three-phase load. The loads under consideration were R and RLC type loads. The connection to the R load can be simulated using the standard PMSG dq model and Park transformations, but the connection to the RLC load requires the modeling of the system as a whole. LabVIEW simulations of these models were used in order to study the behavior of the PMSG under various load conditions (R, RL and RC). These models can be used to simulate the behavior of the PMSG in a conventional (natural gas, coal, etc.) or renewable (wind energy, hydropower, etc.) energy conversion system. They can also be used in designing the control of the energy conversion system (MPPT control, Droop control, etc.).
Seeing the Ocean through Sea Ice

Signatures of submesoscale ocean filaments are commonly observed in satellite images of thin, sparse sea ice. However, sea ice is not a passive tracer, and its distribution over regions of surface ocean convergence depends on a balance between the external ocean drag and the internal stresses due to ice-floe interactions. Therefore, the distribution of sea ice thus depends on ice-ocean interactions at the submesoscale. This study looks at two idealized cases. The first is a convergent filament with surface flow that is independent from the sea-ice. Sea ice accumulates over the center of the filament, and shows multi-timescale behavior. The second case is a filament where the surface flow is driven by ocean dynamics through Ekman transport. It is shown that the coupled ice-ocean system is able to accumulate sea-ice for certain rheological regimes. The results of these two idealized test cases show that concentrations of sea ice over filaments are able to develop on timescales of 1-2 days, and that the peak of the concentration profile can be several times the background. This gives an indication that snapshots of sea ice could be used to constrain surface ocean currents.
The process must fit the problem: Integrating root cause analysis with the system dynamics modeling process for difficult problems

System dynamics has the theoretical potential to productively model any dynamic social problem where entity flow can be aggregated without significant loss of information and to offer practical solution strategies based on the model. However, in practice, as Jay Forrester observed, the field is presently stagnated “on a rather aimless plateau. there is very little penetration into the big issues. ” We argue the central reason is that for the more difficult problems, the present modeling process does not fit the problem because it lacks root cause analysis. This too often results in models that omit a problem’s root causes and therefore the correct high leverage points. The paper begins the conversation for filling this gap by presenting an educational example of a comprehensive process for integrating root cause analysis into the system dynamics modeling process. Abbreviations: root cause analysis (RCA), System Improvement Process (SIP), social force diagram (SFD), integrated system model (ISM). NOTE: The paper is running too long. It will be shortened once we fix its various problems and finalize the main argument. A series of increasingly more focused questions The paper explores a deeply fundamental issue. As a tool for modeling and solving social problems of a dynamic nature, system dynamics offers enormous potential. Models with “aggregated human actions” as well as other aggregated behaviors “are at least potentially better representations that any others” for solving social system problems (Meadows, 1980, p. 26). However, this potential has not been realized in society’s largest problems, those of such scale and public interest they must be addressed by governments. Fifty years after the birth of system dynamics, Forrester (2007) observed that while there are many applications of system dynamics in government, “there is very little penetration into the big issues” and stated the research question this paper attempts For the System Dynamics Review Started August 16, 2021 Present version September 22, 2021
Use of Long-Acting Injectable Antipsychotics in Inpatients with Schizophrenia Spectrum Disorder in an Academic Psychiatric Hospital in Switzerland

Long-acting injectable antipsychotics (LAIs) offer many benefits to patients with schizophrenia spectrum disorder (SSD). They are used with very different frequencies due to questions of eligibility or patients and prescribers’ attitudes towards LAI use. We assessed the prescribing rates of LAIs in a large academic psychiatric hospital with a public service mandate in Switzerland and compared them with other countries and health care systems. To our knowledge, this study is the first to investigate inpatient LAI use in Europe. Medical records of all patients diagnosed with SSD discharged from the Clinic of Adult Psychiatry of the University Hospital of Psychiatry Zurich over a 12 month period from January to December 2019 were evaluated regarding the prescribed antipsychotics at the time of discharge. The rates of use of LAIs among all patients and among patients receiving LAI-eligible antipsychotic substances were assessed retrospectively. We assessed records of 885 patients with SSD. Among all cases, 13.9% received an LAI. Among patients who received antipsychotic medication that was eligible for LAI use (n = 434), 28.1% received an agent as an LAI. LAI use included paliperidone palmitate (69.9%), aripiprazole monohydrate (14.6%), risperidone (4.9%) and first-generation LAIs (9.8%). Compared to international frequencies of LAI administration, the prescription rate of LAIs in SSD patients was low. Further studies will evaluate patient- and prescriber-related reasons for this low rate.
RatsVirus Infection to Induce Diabetes in BBDR TLR Activation Synergizes with Kilham Rat

Virus infection is hypothesized to be an important environmental “trigger” of type 1 diabetes in humans. We used the BBDR rat model to investigate the relationship between viral infection and autoimmune diabetes. BBDR rats are diabetes-free in viral Ab-free housing, but the disease develops in (cid:1) 30% of BBDR rats infected with Kilham rat virus (KRV) through a process that does not involve infection of pancreatic (cid:1) cells. Pretreatment with polyinosinic-polycytidylic (poly(I:C)), a ligand of TLR3, acts synergistically to induce diabetes in 100% of KRV-infected rats. The mechanisms by which KRV induces diabetes and TLR3 ligation facilitates this process are not clear. In this study, we demonstrate that activation of the innate immune system plays a crucial role in diabetes induction. We report that multiple TLR agonists synergize with KRV infection to induce diabetes in BBDR rats, as do heat-killed Escherichia coli or Staphylococcus aureus (natural TLR agonists). KRV infection increases serum IL-12 p40 in a strain-speciﬁc manner, and increases IL-12 p40, IFN- (cid:2) -inducible protein-10, and IFN- (cid:2) mRNA transcript levels, particularly in the pancreatic lymph nodes of BBDR rats. Infection with vaccinia virus or H-1 parvovirus induced less stimulation of the innate immune system and failed to induce diabetes in BBDR rats. Our results suggest that the degree to which the innate immune system is activated by TLRs is important for expression of virus-induced diabetes in genetically susceptible hosts. The Journal of Immunology, 2005, 174: 131–142.
Lecture 3: Kernel Regression

Thus far, we have developed the linear regression framework, which identifies a line of best fit to map from samples to labels. However, in modern datasets, e.g. image classification, it is rarely the case that there exists an effective linear mapping from samples to labels. Hence, we now extend from linear regression to kernel regression to learn a nonlinear mapping from samples to labels. We conclude this lecture by presenting a connection between solving kernel regression and training the last layer of an infinitely wide neural network. Such a connection will lead naturally into the development of Neural Network Gaussian Processes (NNGP) and the Neural Tangent Kernel.
Attention-based Assisted Excitation for Salient Object Detection

Visual attention brings significant progress for Convolution Neural Networks (CNNs) in various applications. In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs. In this mechanism, the activations of object locations are excited in feature maps. This mechanism is specifically inspired by attention-based gain modulation in object-based attention in brain. It facilitates figure-ground segregation in the visual cortex. Similar to brain, we use the idea to address two challenges in salient object detection: gathering object interior parts while segregation from background with concise boundaries. We implement the object-based attention in the U-net model using different architectures in the encoder parts, including AlexNet, VGG, and ResNet. The proposed method was examined on three benchmark datasets: HKU-IS, MSRB, and PASCAL-S. Experimental results showed that our inspired method could significantly improve the results in terms of mean absolute error and F-measure. The results also showed that our proposed method better captured not only the boundary but also the object interior. Thus, it can tackle the mentioned challenges.
Impact of military training stress on hormone response and recovery

Objectives Military personnel are required to train and operate in challenging multi-stressor environments, which can affect hormonal levels, and subsequently compromise performance and recovery. The aims of this project were to 1) assess the impact of an eight-day military training exercise on salivary cortisol and testosterone, 2) track the recovery of these hormones during a period of reduced training. Methods This was a prospective study whereby 30 soldiers (n = 27 men, n = 3 women) undergoing the Australian Army combat engineer ‘Initial Employment Training’ course were recruited and tracked over a 16-day study period which included an eight-day military training exercise. Non-stimulated saliva samples were collected at waking, 30 min post waking, and bedtime on days 1, 5, 9, 13, 15; measures of subjective load were collected on the same days. Sleep was measured continuously via actigraphy, across four sequential study periods; 1) baseline (PRE: days 1–4), 2) field training with total sleep deprivation (EX-FIELD: days 5–8), 3) training at simulated base camp with sleep restriction (EX-BASE: days 9–12), and 4) a three-day recovery period (REC: days 13–15). Results Morning cortisol concentrations were lower following EX-FIELD (p<0.05) compared to the end of REC. Training in the field diminished testosterone concentrations (p<0.05), but levels recovered within four days. Bedtime testosterone/cortisol ratios decreased following EX-FIELD and did not return to pre-training levels. Conclusions The sensitivity of testosterone levels and the testosterone/cortisol ratio to the period of field training suggests they may be useful indicators of a soldier’s state of physiological strain, or capacity, however inter-individual differences in response to a multi-stressor environment need to be considered.
COVID-19 and social determinants of health: Medicaid managed care organizations’ experiences with addressing member social needs

Background The significant adverse social and economic impact of the COVID-19 pandemic has cast broader light on the importance of addressing social determinants of health (SDOH). Medicaid Managed Care Organizations (MMCOs) have increasingly taken on a leadership role in integrating medical and social services for Medicaid members. However, the experiences of MMCOs in addressing member social needs during the pandemic has not yet been examined. Aim The purpose of this study was to describe MMCOs’ experiences with addressing the social needs of Medicaid members during the COVID-19 pandemic. Methods The study was a qualitative study using data from 28 semi-structured interviews with representatives from 14 MMCOs, including state-specific markets of eight national and regional managed care organizations. Data were analyzed using thematic analysis. Results Four themes emerged: the impact of the pandemic, SDOH response efforts, an expanding definition of SDOH, and managed care beyond COVID-19. Specifically, participants discussed the impact of the pandemic on enrollees, communities, and healthcare delivery, and detailed their evolving efforts to address member nonmedical needs during the pandemic. They reported an increased demand for social services coupled with a significant retraction of community social service resources. To address these emerging social service gaps, participants described mounting a prompt and adaptable response that was facilitated by strong existing relationships with community partners. Conclusion Among MMCOs, the COVID-19 pandemic has emphasized the importance of addressing member social needs, and the need for broader consideration of what constitutes SDOH from a healthcare delivery standpoint.