Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Resist Misinformation: A Field Experiment in India ∗

Can people learn to identify misinformation, and to what extent can this mitigate its effects on beliefs, attitudes, and behavior? We conduct a large field experiment with an intervention aimed at improving people’s ability to discern the veracity of information they encounter on social media and reduce their misperceptions about minorities. The experiment is done in India, a region with high levels of misinformation on social media, a significant portion of which targets Muslims, the largest religious minority in the country. The intervention is to provide weekly digests with summaries of fact-checks of viral misinformation, along with narrative explainers on these issues with a lot of misinformation around them. We find that the intervention increases people’s ability to correctly identify misinformation as false by eleven percentage points. However, it also decreases belief in true news by four percentage points. We estimate a structural model to disentangle the two mechanisms of impact—truth discernment, which is the ability to distinguish between false and true news; and skepticism, which changes the overall credulity for both false and true news. The impact is driven by both an increase in truth discernment and skepticism. As people’s factual beliefs become more accurate, it also leads to changes in policy attitudes and behavior. Treated individuals are less likely to support discriminatory policies against Muslims and are more likely to pay for efforts to prevent harassment of inter-faith couples.
Sensory analysis of probiotic functional kulfi by using Indian blackberry (Syzygium cumini L.)

Kulfi is defined as an indigenous product which resembles ice-cream or milk ice. Kulfi is the foremost Indian frozen desert which helps to develop small scale industry and generate sizeable employment and income. It’s origin is uncertain, but it could have become widely used only after artificial ice became available. Kulfi is a nut ice-cream, frozen in small containers of conical shape. Sweetened milk, containing 20-25 percent added sugar is concentrated to about half of its volume and malai, crushed almonds, pistachio and flavouring materials such as vanilla and essence of rose are added after cooling. The present study was made with an attempt to develop a probiotic functional kulfi by using Indian blackberry ( Syzygium cumini L.). Studied for its sensory properties such as color and appearance, flavour and taste, consistency and over all acceptability by trained panelist using 9 point hedonic scale. In the present investigation treatment K 0 is the control kulfi that doesn’t contain any probiotic culture and powder of Indian blackberry and treatments K a to K r are the experimental kulfi which contain various proportion of sugar (10%, 15% and 20%), Indian blackberry powder (2%, 4% and 6%) and probiotic culture (1% ., Lactobacillus acidophilus and Bifidobacterium bifidus ). After sensory evaluation it was found that among all treatments K k scored higher in sensory evaluation and was considered as optimized product. The overall acceptability score of this treatment K r was (8.80) as compared to control K 0 (7.02).
CMOS Voltage Reference Charge Reference Amplifier Design Charge-Pump Circuit

– The sub-threshold CMOS voltage reference circuit is fabricates using a 50nm technology. Voltage reference circuit with minimum supply voltage is design by replacing the Analog amplifier in the conventional CMOS voltage reference circuit with a low-voltage comparator, a charge-pump circuit, and a digital control circuit. pump circuit is design with three state level. The charge pump circuit either charges or discharges a capacitor with voltage or current pulses .
Design and Implementation of a LIDAR Based Range Sensor System

— Positioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensor was used to implement the set-up. For verification of the proposed setup, different scan matching methods for odometry determination in indoor and outdoor environments are tested.
Enabling Microservice Interactions within Heterogeneous Edge Infrastructures

Distributed software laying across a heterogenous node network represents a challenge from many points of view. Microservices need to interact seamlessly, abstracting the infrastructure beneath and scaling at will while transparently balancing the traffic across multiple instances. In edge environments, nodes may move across network boundaries, have limited or no visibility, and turn suddenly unavailable. Developers and users cannot tolerate a decrease in the QoS but expect unconditional service availability. While such an environment provides benefits regarding the close positioning of the computation concerning the final user, there is still a lot of research in progress to find suitable strategies to enable a transition, or even better, a continuum, from the cloud to the edge. Bringing applications closer to the user comes with many efforts from the point of view of the communication. This work presents a set of networking components that overcomes the constraints of the edge environment, bringing further flexibility in the way the communication is carried out. The proposed approach exploits the positioning and the hardware capabilities of the nodes hosting the microservices instances to provide a powerful instrument for edge networking. Through semantic virtual addressing and distributed edge proxying, the services can interact and systematically specify the routing directives. Packets travel across a dynamic tunneled overlay that abstracts the networking discrepancies between the node’s clusters. Service interactions happen transparently thanks to the L4 implementation and the fallback strategies that route the traffic to the most suitable available instances masquerading the network failures, even allowing distributed communication across nodes with no cloud support. The implementation has shown outstanding balancing capabilities even across multiple constrained nodes with limited visibility, performing the routing decisions even 20% faster than the best-performing competitor.
Self-Supervised Deep Learning for Fluorescence Microscopy Denoising

This work modiﬁes existing self-supervised de-noising architectures for Poisson noise typical within low-intensity Fluorescence Microscopy (FM) imagery. Biologists are often required to use low-exposure imaging technology, such as Fluorescence Microscopy, to preserve the integrity of sensitive samples. While these advancements have allowed for broader biological analysis, the collected data is usually corrupted by noise that ob-scures valuable biological insight. Furthermore, ground truth images of live cells are nearly impossible to gather without causing permanent damage to the observed specimen. FM data is unique in that the signal captured is both inherently weak and dominated by Poisson noise rather than Gaussian noise characteristic of traditional optical microscopy. Although many deep learning denoising algorithms exist, namely self-supervised meth-ods in which elusive ground-truth images are not needed, no models directly address the Poisson component abundant in FM data. This work utilizes a novel non-parametric probabilistic prior and posterior approximation to restore FM imagery speciﬁcally dominated by Poisson noise.
Connectivity properties of some transformation graphs

Many combinatorial problems can be formulated as \can we transform configuration 1 into configuration 2 if certain transformations are allowed? " In order to study such questions, we introduce a so-called transformation graph. This graph has the set of all possible configurations as its vertex set, and there is an edge between two configurations if one configuration can be obtained from the other by one of the allowed transformations. Then a question like \can we go from one configuration to another one" becomes a question about connectivity properties of transformation graphs. In this thesis, we study the following types of transformation graphs in particular: Labelled Token Graphs: Here configurations are arrangements of labelled tokens on a given graph, and we can go from one arrangement to another one by moving one token at a time along an edge of the given graph. We classify all cases when labelled token graphs are connected, and classify all pairs of arrangements that are in the same component. We also look at the problem how hard it is to determine the length of the shortest path between two arrangements. Strong k-Colour Graphs: For this transformation graph, the configurations are the proper vertex-colourings of a given graph with k colours, in which all k colours are actually used. We call such a colouring a strong k-colouring. We study the problem when we can transform any strong k-colouring into any other one by recolouring one vertex at a time, always maintaining a strong k-colouring. For certain classes of graphs, we can completely determine when the transformation graph of strong k-colourings is connected.
Validation and cross-cultural adaptation of the Turkish version of the revised Leeds Disability Questionnaire in patients with ankylosing spondylitis

Background/aim The revised Leeds Disability Questionnaire (RLDQ) is a unique assessment tool for patients with ankylosing spondylitis (AS); its comprehensive structure includes posture and neck flexibility parameters. The aim of the study was to determine the psychometric properties of the Turkish RLDQ in patients with AS. Materials and methods A total of 100 AS patients were enrolled in the study. In the first evaluation, patients filled out the Dougados Functional Index (DFI) and Bath Ankylosing Spondylitis Functional Index (BASFI), Stanford Health Assessment Questionnaire (HAQ) in addition to RLDQ. Then, patients were refilled the revised RLDQ in the second assessment. Results The mean age of the patients (40 women, 60 men) was 48.3 ± 12.6 years. The test-retest reliability and internal consistency of the RLDQ total score were excellent. ICC score and Cronbach’s alpha score were calculated as 0.853 and 0.905, respectively. The SEM and MDC values calculated for the RLDQ total score were 2.74 and 7.60, respectively. RLDQ had degrees of correlation with DFI, HAQ, and BASFI of 0.814, 0.742, and 0.852, respectively. Construct validity was excellent (r > 0.50, p < 0.01). Conclusion The Turkish version of the RLDQ was found to be valid and reliable in patients with AS. It should be emphasized that the RLDQ is a distinctive and valuable tool that focuses separately on neck, posture, or other mobility parameters in the clinical assessment of AS.
Fast compound graph layout with constraint support (Kısıt destekli hızlı bileşik çizge yerleştirme)

FAST COMPOUND GRAPH LAYOUT WITH CONSTRAINT SUPPORT Hasan Balcı Ph.D. in Computer Engineering Advisor: Uğur Doğrusöz August 2022 Visual analysis of relational data becomes more challenging in today’s world as the amount of data increases exponentially. Effective visual display of such data is therefore a key requirement to simplify the analysis process. Compound graphs present a practical structure for both representing the relational data with varying levels of groupings or abstractions and managing its complexity. In addition, a good automatic layout of these graphs lets users understand relationships, uncover new insights and find important patterns hidden in the data. To this end, we introduce a new layout algorithm named fCoSE (fast Compound Spring Embedder) for compound graphs with support for user-specified placement constraints. fCoSE combines the speed of spectral layout with the aesthetics and quality of force-directed layout while satisfying specified constraints and properly displaying compound structures. The algorithm first generates a draft layout with the help of a spectral approach, then enforces placement constraints by using newly introduced heuristics and finally polishes the layout via a force-directed layout algorithm modified to maintain enforced constraints. Our experiments performed on both real-life and randomly generated graphs verify that fCoSE outperforms its competitors in terms of both speed and generally accepted graph layout criteria and is fast enough to be used in interactive applications with small to mediumsized graphs.
NLP-22 Detecting Semantically Similar Questions on Quora Dataset

Websites like quora provide a platform for Question and Answering across various subjects and ﬁelds. However, the websites also contain loads of similar questions which have diﬀerent sets of replies although they seek same answer. We aim to develop an application which can detect whether two questions on quora are semantically similar. More formally we want a model which when given two questions q 1 and q 2 , gives f ( q 1 , q 2 ) = (cid:40) 0 if question can’t be answered by the same answer 1 if question can be answered by the same answer depending on whether the questions are similar in terms of meaning or not. The criteria for two questions being semantically similar is that they seek same answer. [1]
A simple underidenti(cid:133)cation test for linear IV models, with an application to dynamic panel data models (cid:3)

For linear IV models it is shown that standard underidenti(cid:133)cation tests like the Cragg-Donald and Kleibergen-Paap tests are Sargan type tests for instrument-error orthogonality in the linear IV model of one endogenous explanatory variable regressed on the others, estimated by LIML. This insight is then used to extend these type of tests to more complex data structures, like dynamic panel data models, leading to very simple to calculate tests for underidenti(cid:133)cation.
Underlagsrapport 2021:1 Barriers to a well-functioning digital market

With growing access to the internet globally, online shopping is on the rise and the number of digital consumers is increasing every year. This trend seems to persist, and it is predicted to accelerate further in the future. However, this growth is associated with certain risks to the well-being of both consumers and markets. One such risk relates to inefficient information disclosures and tricky user interface designs in digital applications that may cause consumer detriment and, consequently, negatively impact the digital market. In this research, we attempted to shed light on two issues that might prompt such adverse effects—dark patterns and incomprehensive terms and conditions. This report intends to identify barriers to the well-functioning digital market through a review of existing research and a qualitative empirical study (expert interviews) and provide guidance on overcoming such obstacles. Further, by applying a behavior change framework, we identified what measures could be taken, mainly by regulators, to ensure a well-functioning market. A systematic literature review was conducted to identify dark patterns in digital markets. Based on the findings, we re-categorized the identified dark patterns according to their impact on consumers and mapped them to the behavior change COM-B (Capability, Opportunity, Motivation-Behavior) model and behavioral change wheel. Additionally, our literature review and a series of expert interviews were used to recognize barriers to understanding and acting according to the disclosures included in terms & conditions agreements. Based on our findings, we present a discussion about the concept of consumer vulnerability, which is critical to understanding consumer detriment. Moreover, our thematic analysis of the expert interviews defines the obstacles in the current policies and intervention functions that might indirectly and directly negatively influence consumers’ choices. We summarize and discuss our findings by presenting a list of barriers and recommendations to overcome them, as proposed by our experts. These research findings might be useful to policymakers and regulatory bodies and could be utilized as a basis for future research on this topic.
International Journal of Multidisciplinary Research and Growth Evaluation www.allmultidisciplinaryjournal.com

ISSN (online): 2582-7138 Volume: 03 Issue: 03 May-June 2022 Received: 27-04-2022; Accepted: 13-05-2022 Page No: 345-358 Abstract This study investigated information sharing and use of health care workers in primary health care centres in Oyo State, Nigeria. The descriptive survey research design of the correlational type was adopted for the study. The population of the study comprised 1787 health care workers spread across 33 primary health care centers (PHCCs) in the headquarters of the Local Government Areas (LGAs) in Oyo State, Nigeria. Sample size of 631 was used for the study and questionnaire was used for data collection. Findings from the study revealed work related issues, problem solving, provision of forum for discussion of ideas and exchange for a reward as major purposes for which primary health care workers in PHCCs Oyo State, Nigeria shared information among themselves just as a high level of information sharing was established amongst them. Also, findings from the study established major purposes of information use by the health care workers as including performance of difficult and technical tasks, helping to influence others to translate vision into action, to come up with unique ideas and to adapt, and work with colleagues and other stakeholders in the sector. On the frequency of use of information by the health care workers, the study revealed occasional use but a positive significant relationship was established between information sharing and use. The study recommended the provision of relevant information management facilities such as computers and related technologies by the management of primary healthcare centres in Oyo State, Nigeria for effective information sharing and use among the primary healthcare workers.
Valley splitting in strained silicon quantum wells

A theory based on localized-orbital approaches is developed to describe the valley splitting observed in silicon quantum wells. The theory is appropriate in the limit of low electron density and relevant for quantum computing architectures. The valley splitting is computed for realistic devices using the quantitative nanoelectronic modeling tool NEMO. A simple, analytically solvable tight-binding model reproduces the behavior of the splitting in the NEMO results and yields much physical insight. The splitting is in general nonzero even in the absence of electric field in contrast to previous works. The splitting in a square well oscillates as a function of S, the number of layers in the quantum well, with a period that is determined by the location of the valley minimum in the Brillouin zone. The envelope of the splitting decays as S−3. The feasibility of observing such oscillations experimentally in Si/SiGe heterostructures is discussed.
Allele frequency differences of causal variants have a major impact on low cross-ancestry portability of PRS

Genome-wide association studies (GWAS) are overwhelmingly biased toward European ancestries. Nearly all existing studies agree that transferring genetic predictions from European ancestries to other populations results in a substantial loss of accuracy. This is commonly referred to as low portability of polygenic risk scores (PRS) and is one of the most important barriers to the ethical clinical deployment of PRS. Yet, it remains unclear how much various genetic factors, such as linkage disequilibrium (LD) differences, allele frequency differences or causal effect differences, contribute to low PRS portability. In this study, we used gene expression levels in lymphoblastoid cell lines (LCLs) as a simplified model of complex traits with minimal environmental variation, in order to understand how much each genetic factor contributes to PRS portability from European to African populations. We found that cis-genetic effects on gene expression are highly similar between European and African individuals ({rho} _g=0.95, S.E.=0.09). This stands in stark contrast to the very low estimates of cis-genetic correlation between Europeans and Africans in previous studies, which we demonstrate are artifacts of statistical bias. We showed that portability decreases with increasing LD differences in the cis-regions. We also found that allele frequency differences of causal variants have a striking impact on PRS portability. For example, PRS portability is reduced by more than 32% when the causal cis-variant is common (minor allele frequency, MAF > 5%) in European samples (training population) but is rarer (MAF < 5%) in African samples (prediction population). While large allele frequency differences can decrease PRS portability through increasing LD differences, we also show that causal allele frequency can significantly impact portability independently of LD. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability caused by causal allele frequency differences. Lastly, we also found that causal allele frequency is the main genetic factor underlying differential gene expression levels across ancestries. We conclude that causal genetic effects are highly similar in Europeans and Africans, and low PRS portability is primarily due to allele frequency differences.
Prevalence and associated factors of depression among junior healthcare professionals of public teaching hospitals of Bangladesh: An analytical cross-sectional study

Due to the significant number and its effects on quality of life, depression is becoming a major concern worldwide. Though its prevalence among junior healthcare professionals is also increasing day by day, still very few data are available regarding this. So, we have conducted a study to find out the prevalence and associated factors of depression among this vulnerable population. A total of 218 participants were enrolled from two public teaching tertiary-level hospitals in Dhaka, Bangladesh from October 2018 to April 2019. Data were collected by using a self-administered questionnaire including the WHO-5 well-being index. Prevalence of major depression was found at 17.9% and poor-emotional well-being was 25.2%. Factors associated independently with major depression were those thinking to be a doctor as the wrong decision (aRRR: 6.85, 95% CI: 1.40-33.45, p=0.017) and taking sedative or anxiolytic drugs (aRRR: 4.54, 95% CI: 1.50-13.73, p=0.007). On the other hand, doing physical exercise (aRRR: 0.32, 95% CI: 0.12-0.89, p=0.028) and being satisfied in their current job position (aRRR: 0.07, 95% CI: 0.02-0.29, p<0.001) had significantly less chance of being suffering of major depression. Suicidal and self-hurting ideation was also found among 23.4% of participants. If these modifiable factors can be addressed properly and by taking necessary steps against these simply identifiable factors, unwanted incidences can be prevented especially in low- and middle-income countries.