Miracle-dz/UniFAR-FTUs huggingface dataset:
Title: Mitigation strategies against Fake News diffusion in online social platforms

Context: Fake News (FN) are claims that are factually wrong and that have the intention to mislead an audience (see [1] and references therein). This malicious behaviour is extremely prevalent on social media platforms like Twitter and Facebook. In fact, the majority of people with online access will partly or fully be informed through these platforms, posing a serious risk on how social opinions are formed since FN are often merged with truthful information which makes them hard to assess by a victim. Indeed, very recent events confirm their power. For example, it has been reported a deluge of misinformation related to Covid-19 [2] and vaccines [3] occurred in social platforms. Equally important is the case of electoral campaigns: the authors of [4] showed that among 171 million tweets related to the US 2016 elections, 25% of them were FN; and similar observations have been observed in the French and Italian elections [10]. The word “infodemic” has been coined to describe this pandemic of FN, which indeed have been shown to propagate faster, and deeper than real news [5]. In sum, studying the spreading of FN and their quick detection is a subject of utmost importance.
Constraining continuous topology optimizations to discrete solutions for photonic applications

: Photonic topology optimization is a technique used to ﬁnd the electric permittivity distribution of a device that optimizes an electromagnetic ﬁgure-of-merit. Two common techniques are used: continuous density-based optimizations that optimize a grey-scale permittivity deﬁned over a grid, and discrete level-set optimizations that optimize the shape of the material boundary of a device. More recently, continuous optimizations have been used to ﬁnd an initial seed for a concluding level-set optimization since level-set techniques tend to beneﬁt from a well-performing initial structure. However, continuous optimizations are not guaranteed to yield suﬃcient initial seeds for subsequent level-set optimizations, particularly for high-contrast structures, since they are not guaranteed to converge to solutions that resemble only two discrete materials. In this work, we present a method for constraining a continuous optimization such that it converges to a discrete solution. This is done by inserting a constrained sub-optimization at each iteration of an overall gradient-based optimization. This technique can be used purely on its own to optimize a device, or it can be used to provide a nearly discrete starting point for a level-set optimization.
What Storage? An Empirical Analysis of Web Storage in the Wild

We Abstract —In this paper we perform the first empirical analysis of the use of web storage in the wild. By using dynamic taint tracking at the level of JavaScript and by performing an automated classification of the detected information flows, we shed light on the key characteristics of web storage uses in the Tranco Top 5k. Our analysis shows that web storage is routinely accessed by third parties, including known web trackers, who are particularly eager to have both read and write access to persistent web storage information. This motivates the need for further research on the security and privacy implications of web storage content.
TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to preceding inertial perturbations. We also expand 2D sequence learning to 3D using a model-free barometric g-h filter robust to inertial and environmental variations. We evaluate TinyOdom for a wide spectrum of inertial odometry applications and target hardware against competing methods. Specifically, we consider four applications: pedestrian, animal, aerial, and underwater vehicle dead-reckoning. Across different applications, TinyOdom reduces the size of neural inertial models by 31× to 134× with 2.5m to 12m error in 60 seconds, enabling the direct deployment of models on URC devices while still maintaining or exceeding the localization resolution over the state-of-the-art. The proposed barometric filter tracks altitude within ±0.1m and is robust to inertial disturbances and ambient dynamics. Finally, our ablation study shows that the introduced magnetometer, physics, and velocity-centric sequence learning formulation significantly improve localization performance even with notably lightweight models.
Development of failure mode and effects analysis using fuzzy analytical network process

Failure mode and effects analysis (FMEA) is a risk assessment tool that decreases potential failures in systems, processes, designs or services has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticised to have many deficiencies various risk priority models have been proposed in the literature to increase the performance of FMEA. In a traditional FMEA, for each failure modes, three risk factors of severity (S), occurrence (O), delectability (D) are evaluated a risk priority number (RPN) is obtained by multiplying these factors. Significant efforts have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. In this study, a fuzzy approach, allowing experts to use linguistic variables for determining pair-wise comparisons, was considered for FMEA by applying fuzzy preference priority (FPP) technique integrated with fuzzy analytical network process (ANP). The case study demonstrated the applicability of the model in FMEA under fuzzy environment.
Exploring Strategies for Sustainable Design and Deployment of Novel Haptic Interfaces

The vast and complex design space for haptic technology demands a diversity in form factors, type of stimuli and application contexts. While research has introduced a large variety of different haptic interfaces as a response, the number of technical contributions has produced a new challenge for reusing and sharing new hardware designs among researchers. In this workshop paper, we identify typical adoption barriers and discuss new strategies and ideas for the design process of haptic devices, to enable more sustainable growth for the design of new haptic technology.
Corporate Social Responsibility and Innovation strategies and their impact on companies’ financial performance

Purpose : The aim of this study is to understand if the relationship between Corporate Social Responsibility (CSR) and Innovation leverages company’s financial performance, thus helping company managers making better strategic decisions. Methodology : To analyze the central hypothesis of this study, data was collected from Thomson Reuters Eikon Datastream on the 1000 largest companies listed on the stock exchange. Once the data was organized, hierarchical linear regressions were performed to study the effects. Results : Strategies simply focused on CSR show inconclusive results in their relationship with financial performance, while strategies involving CSR and Innovation appear to be the most beneficial to companies. Findings: The involvement in CSR activities is accompanied by costs that can diminish the financial performance of companies when not well supported by policies. The combination of CSR and Innovation appears to be the best strategy for companies to seek improvements in their financial performance. Limitations : This study is essentially focused on large companies. It remains to be understood whether the impact of these activities on smaller companies will be felt the same way. Regarding the data collected, variables used to measure innovation performance have some limitations on the amount of information available compared to the other variables. Policy recommendations: The positive values of the relationship between CSR and Innovation represent a good foundation for policy makers to continue to promote and improve CSR in companies. It is important that there is support from the government so that the impact of these activities does not take so long to be felt by companies. It is important that there is a greater effort by policy makers to create responsible policies that promote equal levels of CSR around all regions.
Disaster Plans, Preparedness And Response: A Case Study Of The Mutare City Council Clinics And Hospitals In 2020

- Objective: To examine the Mutare City Council Clinics and Hospitals’ level of disaster preparedness. This was done by exploring: the availability of disaster plans, frequency of disaster preparedness workshops, trainings and drills, staff knowledge and experience on disaster preparedness and response and the adequacy of material resources needed for disaster response. Design: The mixed methods approach was used in this research. Both quantitative and qualitative data was collected in this investigation. The descriptive research design was used and the study was based on the positivism philosophy. Setting: The target population were all the 108 healthcare staff working in the City of Mutare Council clinics and hospitals at the time of study. The researcher aimed at collecting data from all the staff in 8 clinics and one hospital. Methods: Questionnaires with closed ended questions and key informant interviews were used to collect data. Data was collected from January to March 2020.The quantitative data was analyzed using the Statistical Package for Social Sciences. Results: Among the clinic/hospital respondents: 99% considered disaster plans as important, 55% indicated that their clinic/hospital had disaster plans; 31% were of the opinion that their clinics and hospitals were fully prepared for emergencies and
Opleiding Informatica Bioinformatics analysis of the core hallmarks of cancer genes

Research into cancer has led to a broad ﬁeld of studies and so there are many diﬀerent ways to do research on cancer. The Hallmarks of Cancer is such a way as it provides a framework to improve the study into the diﬀerent mechanisms and types of cancer. However, due to diﬀerent methods into the mapping of genes to the hallmarks, diﬀerent results can be obtained. Based on the results of [5] a core set of 793 genes is used that has been mapped consistently to hallmarks. The aim of this thesis is therefore to analyse this core set to the diﬀerent hallmarks and the mapping of the genes. With the use of bioinformatics methods, like functional enrichment analysis, the core set and the diﬀerent study methods were compared. This thesis shows that the core set of genes represent the diﬀerent hallmarks. However, this representation of the hallmark is slim and it varies much between the hallmarks.
Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity

Stochastic zeroth-order optimization algorithms have been predominantly analyzed under the assumption that the objective function being optimized is time-invariant. Motivated by dynamic matrix sensing and completion problems, and online reinforcement learning problems, in this work, we propose and analyze stochastic zeroth-order optimization algorithms when the objective being optimized changes with time. Considering general nonconvex functions, we propose nonstationary versions of regret measures based on ﬁrst-order and second-order optimal solutions, and provide the corresponding regret bounds. For the case of ﬁrst-order optimal solution based regret measures, we provide regret bounds in both the low-and high-dimensional settings. For the case of second-order optimal solution based regret, we propose zeroth-order versions of the stochastic cubic-regularized Newton’s method based on estimating the Hessian matrices in the bandit setting via second-order Gaussian Stein’s identity. Our nonstationary regret bounds in terms of second-order optimal solutions have interesting consequences for avoiding saddle points in the nonstationary setting.
An mHealth Lifestyle Intervention Service for Improving Blood Pressure using Machine Learning and IoMTs

In this paper, we present an AI-driven lifestyle intervention service for patients with hypertension. The automated intervention platform consists of a remote monitoring system that ingests lifestyle and blood pressure (BP) data and builds a personalized machine learning (ML) model to generate tailored lifestyle recommendations most relevant to each patient's BP. Lifestyle data is collected from a wearable device and questionnaire mobile app which includes activity, sleep, stress and diet information. BP data is remotely collected using at-home BP monitors. With this data, the system trains random forest models that predict BP from lifestyle features and uses Shapley Value analysis to estimate the impact of features on BP. Precise lifestyle recommendations are generated based on the top lifestyle factors for each patient. To test the system's ability to improve BP, we enrolled hypertensive patients into a three-armed clinical trial. During the 6-month trial period, our system provided weekly recommendations to patients in the experimental group. We evaluate the system's effectiveness based on multiple BP improvement metrics and comparison with a control group. Patients in the experimental group experienced an average BP change of −4.0 and −4.7 mmHg for systolic and diastolic BP, respectively, compared to −0.3 and −0.9 mmHg for the control group. Our results demonstrate that the platform can effectively help patients improve their BP through precise lifestyle recommendations.
The effect of financial incentives on the retention of shortage-subject teachers: evidence from England

School systems often experience shortages of maths and science teachers, reflecting difficulties in both recruiting and retaining people qualified to teach these subjects. In England, teachers with maths and science degrees face a higher outside pay ratio than other teachers and also tend to leave the profession at higher rates. We evaluate a policy aimed at improving retention by providing targeted uplifts in pay worth 8% of gross salary for early-career maths and physics teachers. Leveraging variation in eligibility across time, regions and school subjects, we find that eligible teachers are 23% less likely to leave teaching in state funded schools in years they were eligible for payments. This implies a pay-elasticity-of-exit of -3, which is similar to results from evaluations of similar policies in the United States. Our analysis suggests that the cost per additional teacher retained through the policy is 32% lower than training an equivalent replacement teacher. Taken together, these results suggest that persistent shortages of maths and science teachers can be reduced through targeted pay supplement policies. School systems often experience shortages of maths and science teachers, reflecting difficulties in both recruiting and retaining people qualified to teach these subjects. In England, teachers with maths and science degrees face a higher outside pay ratio than other teachers and also tend to leave the profession at higher rates. We evaluate a policy aimed at improving retention by providing targeted uplifts in pay worth 8% of gross salary for early-career maths and physics teachers. Leveraging variation in eligibility across time, regions and school subjects, we find that eligible teachers are 23% less likely to leave teaching in state funded schools in years they were eligible for payments. This implies a pay-elasticity-of-exit of -3, which is similar to results from evaluations of similar policies in the USA. Our cost effectiveness analysis suggests that the cost per additional teacher retained is 32% lower than the costs of training an equivalent replacement teacher. Taken together, these results suggest that persistent shortages of maths and science teachers can be reduced through targeted pay supplement policies. Acknowledgments: This work Gatsby This work The statistical data work does not imply relation to interpretation or analysis the statistical data. This work uses research which may not exactly reproduce National Statistics aggregates.
MEDIA CONSTRuCTION Of DEVIANCE – OTHERING Of ROMA MINORITy IN TELEVISION NEWS DISCOuRSE

How was the Roma minority represented in the main evening news program Události of Czech Television, the public service media, between 2000 and 2015? This article focuses on the visual representation of Roma and the key signs used in television news reports. It presents findings from analysis based on quantitative content analysis and semiotic analysis. Visual representation of the Roma on television news is dominated by images of groups, children and young adults and shots of the environment and places where they live including clutter and ramshackled houses. On the contrary, representations of Roma as authorities are missing. Analysis revealed a long-term repeated thematization of Roma otherness. Although the voice-over is correct, the visual signs used in reports connote negative meanings and stereotypes. Through such visuals Roma are constructed as culturally different. Thus news accentuated and reproduced negative attitudes that a large part of the Czech majority maintains towards Roma.
Simultaneous Machine Translation with Visual Context

Simultaneous Machine Translation (SiMT) is a subfield of Natural Language Processing that focuses on translating continuous streams of sentences from one language to another with the lowest latency and highest quality possible. Previous works and empirical research have shown that having supplementary information can improve the performance of a language model. The main hypothesis for this project was that having supplementary information from an image will make up for the missing textual information. Language barriers can have multiple implications in fields like healthcare, education, aviation and multilateral negotiations which is why the development of such a system is required. The project has been using the Multi30K dataset and pysimt library for training and evaluation. The first phase of the project was completed in January and an improvement of roughly 2 BLEU points was been achieved with the help of visual context. The next phase of the project looked into several methods that can be used to improve the performance of the SiMT system. On analyzing the Multi30K dataset, other online available datasets as well as recent publications, I came to the conclusion that creating a new dataset for the task of Simultaneous Machine Translation was the best option. Therefore, a new dataset was created by scraping the video from TED Talks. In the end, the dataset contains 4024 videos with subtitles available in several different languages. The dataset is roughly 118GB in size and using a segmentation algorithm, this model can be run on the current SiMT model. The main hypothesis for this part is that TED Talks that contain slides, animations or other resources for explaining the content will provide valuable information in the form of visual features that can be extracted using a ResNet CNN which will improve the performance of the simultaneous machine translation system.
Integrated CoolMOS FET/SiC-diode module for high performance power switching

A Si CoolMOS field effect transistor and SiC diode assembly with gate driver in boost configuration (ratings at 600V/12A), for power factor correction application, has been fabricated in a version of an integrated power electronic module. It uses the so-called embedded power technology, to form a three-dimensional multiple chip/component interconnection with the embedded chips in a co-planar ceramic substrate with thin-film metallization bond/interconnection added on top. In this paper, the switching parameters of this module and their effects on the performance of a converter have been analyzed and experimentally characterized. The procedures adopted for the defined fabrication process of planar metallization interconnects are presented. In addition to the improvement of structural electrical properties, compared to a conventional discrete version, the characteristics of the planar process integration have also been demonstrated.
Point Cloud Completion by Deep Learning

II Today, measuring of buildings and rooms is still undertaken with tachymeters and partly also with measuring tapes. However, in recent years the share of 3D laser scanners has rapidly increased, since they are more accurate, less prone to human error and faster in capturing a scene. When scanning a scene, some parts of the scene are missing due to occlusions by objects. An example of this is a cabinet standing in front of a wall. The 3D scanner then only captures a point cloud with the surface of the cabinet and the surface information of the wall behind it is missing. In order to predict these occluded points in a point cloud, this master thesis analyzes the existing methods of scene completion and proposes a novel approach to convert a point cloud into a truncated signed distance field. This truncated signed distance field is then used on an existing volumetric scene completion network that is fully self-supervised. Additionally, the suitability of the LiDAR sensor of iPhones and iPads for generating a dataset is examined. Abstract
OUTSOURCING DECISIONS IN SINGLE MACHINE SCHEDULING PROBLEM WITH MULTIPLE EXTERNAL FACILITIES

This paper considers outsourcing decisions in a scheduling problem. The objective of the problem is to minimize the outsourcing costs under the constraints of the capacity of outsourcing facilities and due date of each job. The problem is composed of three kinds of decisions. The first decision is the selection of jobs to be processed in-house, the second is to schedule the in-house processing jobs with due date constraints, and the last is to select an outsourcing provider for each outsourced job where the outsourcing provider has a capacity constraint. Some optimality conditions and solution properties for the problem are presented. A solution algorithm with pseudo-polynomial complexity is suggested to find the optimal solution of the problem. The main contributions of this paper are as follows: (1) the mathematical model for the problem is proposed, (2) the pseudo-polynomial algorithm, Depth First Search (DFS) is developed to find the optimal solution and (3) several optimality properties for the problem are addressed. Numerical experiments show that the DFS algorithm has better results than Dynamic Programming (DP) in efficiency even for larger numbers of jobs and outsourcing providers.