Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment

Randomized Controlled Trials, when feasible, give the strongest and most trustworthy empirical measures of causal effects. They are the gold standard in many clinical, social, and behavioral fields of study. However, the most important settings often involve the most sensitive data, therefore cause privacy concerns. In this paper, we outline a way to deploy an end-to-end privacy-preserving protocol for learning causal effects from Randomized Controlled Trials (RCTs). We are particularly focused on the difficult and important case where one party determines which treatment an individual receives, and another party measures outcomes on individuals, and these parties do not want to leak any of their information to each other, but still want to collectively learn a true causal effect in the world. Moreover, we show how such a protocol can be scaled to 500 million rows of data and more than a billion gates. We also offer an open source deployment of this protocol. We accomplish this by a three-stage solution, interconnecting and blending three privacy technologies--private set intersection, multiparty computation, and differential privacy--to address core points of privacy leakage, at the join, at the point of computation, and at the release, respectively. The first stage uses the Private-ID protocol[8] to create a private encrypted join of the users. The second stage utilizes the encrypted join to run multiple instances of a general purpose MPC over a sharded database to aggregate statistics about each experimental group while discarding individuals who took an action before they received treatment. The third stage adds distributed and calibrated Differential Privacy (DP) noise within the final MPC computations to the released aggregate statistical estimates of causal effects and their uncertainty measures, providing formal two-sided privacy guarantees. We also evaluate the performance of multiple open source general purpose MPC libraries for this task. We additionally demonstrate how we have used this to create a working ads effectiveness measurement product capable of measuring hundreds of millions of individuals per experiment.
Handwritten Text Recognition in Historical Documents

After digitalization historical documents are available in the form of scanned images. However, having documents in the form of digital text simplifies the work of historians, as it e.g. makes it possible to search for text. Handwritten Text Recognition (HTR) is an automatic way to transcribe documents by a computer. There are two main approaches for HTR, namely hidden Markov models and Artificial Neural Networks (ANNs). The proposed HTR system is based on ANNs. Preprocessing methods enhance the input images and therefore simplify the problem for the classifier. These methods include contrast normalization as well as data augmentation to increase the size of the dataset. Optionally, the handwritten text is set upright by a deslanting algorithm. The classifier has Convolutional Neural Network (CNN) layers to extract features from the input image and Recurrent Neural Network (RNN) layers to propagate information through the image. The RNN outputs a matrix which contains a probability distribution over the characters at each image position. Decoding this matrix yields the final text and is done by the connectionist temporal classification operation. A final text postprocessing accounts for spelling mistakes in the decoded text. Five publicly accessible datasets are used for evaluation and experiments. Three of these five datasets can be regarded as historical, they are from the 9th century, from the 15th until the 19th century and from around the year 1800. The accuracy of the proposed system is compared to the results of other authors who published their results. Preprocessing methods are optionally disabled to analyze their influence. Different decoding algorithms and language models are evaluated. Finally, the text postprocessing method is analyzed. The four main contributions of this thesis are: (1) analysis of different parameters and different architectures of the proposed system, (2) a word segmentation approach using a decoding method for the RNN output, (3) a CNN based replacement of the RNN layers and (4) a decoding algorithm which integrates a dictionary and language model while still allowing arbitrary non-word characters between words.
A Conceptual Paper: Moderating Effects of Metacognition and Gender on the Association between Emotion Regulation and Self-Efficacy Among Pre-Service Teachers

Pre-service teacher’s self-efficacy plays a vital role in Malaysia educational transformation. Emotion regulation influences one’s ability to cope with the stress and challenges. This research focuses on the two variables which determine pre-service teacher’s self-efficacy and emotion regulation. Metacognition enables pre-service teachers to rationally evaluate their own capability. It serves as an important tool in emotional regulation to engage pre-service teacher in cognitive re-appraisal process. Gender differences lead to different thinking style and emotion regulation strategies. Metacognitive Awareness Inventory (MAI), Emotion Regulation Questionnaire (ERQ) and General Self-Efficacy (GSE) will serve as the instruments in this study. 133 pre-service teachers from Institute of Teacher Education Campus Bahasa Melayu will be selected using clustered random sampling method. The findings of this research can serve as a reference in teacher preparation process. Keywords: metacognition, emotion regulation, self-efficacy, pre-service teacher, gender differences
Effect of glow discharge treatment of substrates on parylene‐substrate adhesion

A detailed investigation of the effect of argon, oxygen, and methane glow discharge (plasma) treatment of substrates on the adhesion characteristics of parylene polymer to a variety of substrates is reported. It is found that methane glow discharge treatment, which results in the deposition of a glow discharge polymer of methane on the substrate surface, is most effective in improving the bonding strength of the parylene polymer, while the oxygen glow discharge treatment which produces a hydrophilic substrate surface with no or insignificant polymer deposition is detrimental to parylene adhesion. The effect of argon glow discharge treatment may vary from true cleaning of the substrate surface to deposition of a thin layer of a polymeric coating produced by the simultaneous glow discharge polymerization of the ablated organic materials existing in the reactor and may or may not improve parylene adhesion. These results are supported by the Auger electron spectroscopy and contact angle measurements of the glow discharge‐treated surfaces. Model studies using substrates of different surface free energies indicate that water‐resistant adhesion of parylene polymer increases with the hydrophobicity of the substrate material and is in harmony with the above findings. From the viewpoint of adhesion the most advantageous and dependable effect is obtained with the methane plasma treatment. The treatment results not only in the formation of a more compatible, hydrophobic surface but also produces radical sites for covalent bonding with the parylene.A detailed investigation of the effect of argon, oxygen, and methane glow discharge (plasma) treatment of substrates on the adhesion characteristics of parylene polymer to a variety of substrates is reported. It is found that methane glow discharge treatment, which results in the deposition of a glow discharge polymer of methane on the substrate surface, is most effective in improving the bonding strength of the parylene polymer, while the oxygen glow discharge treatment which produces a hydrophilic substrate surface with no or insignificant polymer deposition is detrimental to parylene adhesion. The effect of argon glow discharge treatment may vary from true cleaning of the substrate surface to deposition of a thin layer of a polymeric coating produced by the simultaneous glow discharge polymerization of the ablated organic materials existing in the reactor and may or may not improve parylene adhesion. These results are supported by the Auger electron spectroscopy and contact angle measurements of the gl.
Morphological Segmentation of Polysynthetic Languages for Neural Machine Translation: The Case of Inuktitut

Segmentation of words into sub-word units is a crucial preprocessing step of modern machine translation systems, which allows the translation of unseen and rare words. It is still largely unknown how existing methods perform on a category of languages with a much higher degree of morphological complexity, called polysynthetic languages. Characteristic for polysynthetic languages are long sentence-words, that consist of many morphemes. These long words are a result inflection and agglutination, which allow words to carry a much more detailed meaning than words in most other languages can. We hypothesise that, to deal with such complex languages, translation systems require a robust segmentation method to isolate meaningful parts of a word accurately and consistently. The current state-of-the-art of language-agnostic segmenters were not designed with polysynthetic languages in mind, which begs the question whether they can provide adequate performance for these languages. In this thesis, various methods are compared on their ability to generate linguistically correct segmentations, and their ability to produce quality translations as a part of a state-of-the-art neural machine translation system for the low-resource polysynthetic language Inuktitut. With the results we determine whether linguistically correct segmentation is beneficial for the translation performance, and how it affects the translation model’s generalization ability.
TagFi

Tag localization is crucial for many context-aware and automation applications in smart homes, retail stores, or warehouses. While custom localization technologies (e.g RFID) have the potential to support low-cost battery-free tag tracking, the cost and complexity of commissioning a space with beacons or readers has stifled adoption. In this paper, we explore how WiFi backscatter localization can be realized using the existing WiFi infrastructure already deployed for data applications. We present a new approach that leverages existing WiFi infrastructure to enable extremely low-power and accurate tag localization relative to a single scanning device. First, we adopt an ultra-low power tag design in which the tag blindly modulates ongoing WiFi packets using On-Off Keying (OOK). Then, we utilize the underlying physical properties of multipath propagation to detect the passive wireless reflection from the tag in the presence of rich multipath propagations. Finally, we localize the tag from a single receiver by forming a triangle between the tag reflection and the LoS path between the two WiFi transceivers. We implement TagFi using a customized backscatter tag and off-the-shelf WiFi chipsets. Our empirical results in a cluttered office building demonstrate that TagFi achieves a median localization accuracy of 0.2m up to 8 meters range.
’ s repository of research publications and other research outputs Chapter 1 : Introducing systems approaches

The five approaches covered in Systems Approaches to Making Change – System Dynamics (SD) Viable Systems Model (VSM), Strategic Options Development and Analysis (SODA: with cognitive mapping), Soft Systems Methodology (SSM), and Critical Systems Heuristics (CSH) – are introduced. The rationale for their inclusion is described based on their (i) common historic emergence in dealing with complex situations of change and uncertainty, (ii) shared potential and actual constructivist use of the systems idea, and (iii) pedigree of adaptability and versatility of tools in working with other approaches to making change.
On Implementing Ethical Principles in Design Science Research

Technological innovations raise axiological questions such as what is right or wrong, good and bad, and so on (i.e., ethical considerations). These considerations have particular importance in design science research (DSR) projects since the developed artifacts often actively intervene into human affairs and, thus, cannot be free from value. To account for this fact, Myers and Venable (2014) proposed six ethical principles for DSR in order to support researchers to conduct ethical DSR. However, ethical principles per se—and the ethical DSR principles that Myers and Venable proposed— have an abstract nature so that they can apply to a broad range of contexts. As a consequence, they do not necessarily apply to specific research projects, which means researchers need to contextualize them for each specific DSR project. Because doing so involves much challenge, we explore how contemporary DSR publications have dealt with this contextualization task and how they have implemented the six ethical principles for DSR. Our results reveal that DSR publications have not discussed ethical principles in sufficient depth. To further promote ethical considerations in DSR, we argue that both DSR researchers and reviewers should be supported in implementing ethical principles. Therefore, we outline two pathways toward ethical DSR. First, we propose that researchers need to articulate the next generation of ethical principles for DSR using prescriptive knowledge structures from DSR. Second, we propose extending established DSR conceptualizations with an ethical dimension and specifically introduce the concept of ethical DSR process models. With this work, we contribute to the IS literature by reviewing ethical principles and their implementation in DSR, identifying potential challenges hindering efforts to implement ethics in DSR, and providing two pathways towards ethical DSR.
Impact of Environment Over Human Health

Thru this report, both the established as well as the emerging nations carried out a thorough analysis of a financial focused on the effects of climatic change on human health. Environmental ecosystems pose various health and well-being problems, and many of these issues tend to evolve and change in forms which we can accurately anticipate and not create. Therefore, socioeconomic influences have a significant impact on various sections of society.Thru the air quality we consume, nutrition we consume and the water we consume, the physical world contributes enormously to human wellbeing. This provides, firstly, improved environmental options, while, secondly, risks to infrastructure, manufacturing systems, agriculture and waste disposal activities. This provides more commercial and leisure opportunities. Air toxins and possible pathogens can affect the human health across a variety of different mechanisms of transport and exposure. In this review paper first quality of air, water have been thoroughly discussed, initially explaining the changes in air and water over the time then the impact these changes cause to the human health and the improvements that people record in biodiversity or the climate in various habitats have been demonstrated.
University of Dundee The Genomic Impact of Selection for Virulence against Resistance in the Potato Cyst Nematode, Globodera pallida Varypatakis,

Although the use of natural resistance is the most effective management approach against the potato cyst nematode (PCN) Globodera pallida, the existence of pathotypes with different virulence characteristics constitutes a constraint towards this goal. Two resistance sources, GpaV (from Solanum vernei) and H3 from S. tuberosum ssp. andigena CPC2802 (from the Commonwealth Potato Collection) are widely used in potato breeding programmes in European potato industry. However, the use of resistant cultivars may drive strong selection towards virulence, which allows the increase in frequency of virulent alleles in the population and therefore, the emergence of highly virulent nematode lineages. This study aimed to identify Avirulence (Avr) genes in G. pallida populations selected for virulence on the above resistance sources, and the genomic impact of selection processes on the nematode. The selection drive in the populations was found to be specific to their genetic background. At the genomic level, 11 genes were found that represent candidate Avr genes. Most of the variant calls determining selection were associated with H3-selected populations, while many of them seem to be organised in genomic islands facilitating selection evolution. These phenotypic and genomic findings combined with histological studies performed revealed potential mechanisms underlying selection in G. pallida.
Factors explaining high interest rates in Mongolia: A Markov Regime-Switching approach

This paper examines the dynamics of bank lending, deposit rates and interest rate spread in Mongolia using both accounting framework and Markov Regime-Switching approach. The empirical analysis suggests that the high lending rate in Mongolia is compatible with underdeveloped stock market, low savings ratio, inefficient operating costs of banks, high credit risk, high interbank market rate driven by unstable inflation, strong competition in deposit market and concentration in the loan market. Important findings stand out: (i) the accounting framework suggests that bank lending rate is high because of high deposit rate, while high interest rate spread mainly reflects operating costs and provision for loan losses; (ii) the results obtained from Markow Regime-Switching approach support the view that permitting asymmetry and non-linearity in responses of interest rate to changes in explaining factors are important in the interest rate analysis; (iii) effects of non-performing loan, interbank market rate and growth of money supply on the interest rate spread, bank lending and deposit rates differ depending on the regime, while structure of loan and deposit markets, business cycle fluctuations, budget expenditure and stock market capitalization significantly affect both bank lending rate and interest rate spread regardless of the regime; and (iv) market structure, risks associated with lending operations (credit risk, interest rate risk, the interaction between the risks and inflation) and industrial product affect the lending rate transmitting through the interest rate spread, whereas the growth of M2 money and financial sector development influence the lending rate channeling through the deposit rate.
The Association between EWOM, Knowledge Sharing and Customer’s Perspective’s: Brief Systematic Literature Review

The main aim of this study to explore the relation between the EWOM and Knowledge sharing, the study revel that no theoretical relation between EWOM and Knowledge Sharing, on the other hand the study showed that both of the terms EWOM, WOM and Knowledge sharing using the same mechanism for sharing information. Both of them have their impact on customer satisfaction and intention to buy. Due to shortage of information and research related to the topic the topic need to do more research about this missing research area.
Global Ensemble of Temperatures over 1850–2018: Quantification of
Uncertainties in Observations, Coverage, and Spatial modelling
(GETQUOCS)

Instrumental temperature records are derived from the network of in situ measurements of land and sea surface temperatures. This observational evidence is seen as fundamental to climate science. Therefore, the accuracy of these measurements is of prime importance for the analysis of temperature variability. There are spatial gaps in the distribution of instrumental temperature measurements across the globe. This lack of spatial coverage introduces coverage error. An approximate Bayesian 5 computation based multi-resolution lattice kriging is developed and used to quantify the coverage errors through the variance of the spatial process at multiple spatial scales. It critically accounts for the variation in the parameters of this advanced spatial statistics model itself, thereby providing for the first time a full description of both the spatial coverage uncertainties along with the uncertainties in the modelling of these spatial gaps. These coverage errors are combined with the existing estimates of uncertainties due to observational issues at each station location. It results in an ensemble of 100,000 monthly temperatures 10 fields over the entire globe that samples the combination of coverage, parametric and observational uncertainties from 1850 till 2018 over a 5◦× 5◦ grid.
LIMITED ATTENTION AND POST-EARNINGS ANNOUNCEMENT DRIFT: EVIDENCE FROM CHINA’S STOCK MARKET

This paper utilizes Chinese stock data to provide further evidence on the power of limited attention theory in explaining post-earnings announcement drift. As retail investors prevail in China and they are easily distracted by market swings, we should expect severe attention problems, resulting in larger underreaction to firm information and higher sensitivity to market movement, i.e., the so-called “market movement effect”. After accounting for special arrangements such as preannouncements and earnings previews, we confirm a strong presence of this effect in Chinese stock market, given the “Friday effect” and “announcement concentration effect” being controlled for. Moreover, the effect is asymmetric in market up and down, and becomes more pronounced for small-cap and value stocks.
Near Real-time Forgery Detection and Localization in Stereo RGB and 3D LiDAR Data from Autonomous Vehicles

—The widespread availability of applications for forging multimedia data bolsters the need for their detection and localization especially in autonomous decision-making systems such as autonomous vehicles. Traditional and machine learning methods have been researched extensively for the detection of the same in 2D images but are scarce for the 3D point cloud. Recent trends have seen a shift to leverage the power of deep learning. While recent studies have proposed deep learning-based architectures for accurate detection of forgery in 2D, studies in detection and localization in 3D, in general, are still in preliminary stages. We propose a Forgery Detection and Localization network (FDL-Net) that accurately detects in 2D and localizes the forgery simultaneously in 2D and 3D point cloud. Such a Vision Based Measurement (VBM) system can compute the probability of forgery for each pixel in the image and predict the tampered area ultimately. We train FDL-Net on groups of Easy, Medium, and Hard automatically-generated attacks on RGB and point clouds, based on the KITTI Object Recognition dataset. FDL-Net is able to detect and localize forgery with a high Intersection over Union scores of 0.9773, 0.9324, and 0.73451 for each group respectively and localize the region of attack in 2D stereo RGB and 3D point cloud in less than 300 milliseconds. In comparison to current state-of-the-art architectures, FDL-Net is superior in its ability to detect and localize not only Easy, Medium but also Hard attacks that are not visible in most cases to the naked eye. To show that our proposed end-to-end network can be a general approach to segment the forged area within the streaming data, we compare its performance with other state-of-the-art methods on benchmark datasets such as CASIA V1.0 and CASIA V2.0.