Miracle-dz/UniFAR-FTUs huggingface dataset:
Label Inference Attacks Against Vertical Federated Learning

As the initial variant of federated learning (FL), horizontal federated learning (HFL) applies to the situations where datasets share the same feature space but differ in the sample space, e.g., the collaboration between two regional banks, while trending vertical federated learning (VFL) deals with the cases where datasets share the same sample space but differ in the feature space, e.g., the collaboration between a bank and an e-commerce platform. Although various attacks have been proposed to evaluate the privacy risks of HFL, yet, few studies, if not none, have ex-plored that for VFL. Considering that the typical application scenario of VFL is that a few participants (usually two) collaboratively train a machine learning (ML) model with features distributed among them but labels owned by only one of them, protecting the privacy of the labels owned by one participant should be a fundamental guarantee provided by VFL, as the labels might be highly sensitive, e.g., whether a person has a certain kind of disease. However, we discover that the bottom model structure and the gradient update mechanism of VFL can be exploited by a malicious participant to gain the power to infer the privately owned labels. Worse still, by abusing the bottom model, he/she can even infer labels beyond the training dataset. Based on our ﬁndings, we propose a set of novel label inference attacks against VFL. Our experiments show that the proposed attacks achieve an outstanding performance. We further share our insights and discuss possible defenses. Our research can shed light on the hidden privacy risks of VFL and pave the way for new research directions towards more secure VFL.
The Dynamic Relationship Between FDI, ICT, Trade Openness, and Economic Growth: Evidence from BRICS Countries

Information and communication technology (ICT) is one of the primary zones that stimulates economic development in today’s globalized world. It promotes technological developments in worldwide communication and manufacturing systems, as well as economic growth and development. Many economic activities, such as international trade and foreign direct investment, rely heavily on contemporary information and communications technologies (FDI). The goal of this study is to look at the dynamic relationship between FDI, ICT, trade openness, and economic growth in the context of BRICS countries from 2000 to 2018, with Gross Domestic Product as the dependent variable and Telephone subscriptions, Mobile subscriptions, Broadband subscriptions, Internet subscribers, Secure internet servers, Trade, and Foreign direct investment as the independent variables.Two variables are used as proxies to manage the macroeconomic environment, while five variables are used as proxies for ICT infrastructures. The outcomes of this study are analyzed using Generalized Methods of Movements (GMM). According to this study, ICT has a positive impact on the economic growth of a few countries. Trade openness and foreign direct investment, on the other hand, have a negative impact on economic growth. As growing countries, the BRICS must participate in economic reform and liberalization measures. This report suggests policy proposals for improving ICT standards, focusing especially on economic growth, trade openness, and increasing foreign investment in the BRICS countries.
Addressing Repetition in Crowdsourcing: A Concept for Fast-Form Entry

This workshop paper outlines a conceptual browser plugin that enables crowdworkers to store and later rapidly provide personal information frequently requested in crowdsourcing tasks. Personal data, including demographic data such as age and ethnicity, as well as responses to commonly used personality-related survey instruments, is often critical to collect in crowdsourcing tasks but results in a repetitive experience for crowdworkers. From a requesters perspective, this repetition can result in reduced data quality or the decision to abstain from collecting extensive information on the workers completing a given task. Moreover, given the extensive role of crowdworkers in labelling training data for artificial intelligence applications, ensuring awareness of the workers’ characteristics can help alleviate future biases. In this work, we present the motivation and design requirements for this (hypothetical) plugin and seek input from the community towards its future development.
2015 A New Selection Method of Anthropometric Parameters in Individualizing Head-Related Impulse

3 Abstract A trend issue in modeling head-related impulse responses (HRIRs) is how to individualize HRIRs models that are convenient for a particular listener. The objective of this research is to show a robust selection method of eight anthropometric parameters out of all 27 parameters defined in CIPIC HRTF Database. The proposed selection method is systematically and scientifically acceptable, compared to ‘trial and error’ method in selecting the parameters. The selected anthropometric parameters of a given listener were applied in establishing multiple linear regression models in order to individualize his / her HRIRs. We modelled the entire minimum phase HRIRs in horizontal plane of 35 subjects using principal components analysis (PCA). The individual minimum phase HRIRs can be estimated adequately by a linear combination of ten orthonormal basis
On the Complexity of Algebraic Numbers, and the Bit-Complexity of Straight-Line Programs

We investigate the complexity of languages that correspond to algebraic real numbers, and we present improved upper bounds on the complexity of these languages. Our key technical contribution is the presentation of improved uniform TC 0 circuits for division, matrix powering, and related problems, where the improvement is in terms of “majority depth” (initially studied by Maciel and Thérien). As a corollary, we obtain improved bounds on the complexity of certain problems involving arithmetic circuits, which are known to lie in the counting hierarchy, and we answer a question posed by Yap.
Sketch2Pose

Artists frequently capture character poses via raster sketches, then use these drawings as a reference while posing a 3D character in a specialized 3D software --- a time-consuming process, requiring specialized 3D training and mental effort. We tackle this challenge by proposing the first system for automatically inferring a 3D character pose from a single bitmap sketch, producing poses consistent with viewer expectations. Algorithmically interpreting bitmap sketches is challenging, as they contain significantly distorted proportions and foreshortening. We address this by predicting three key elements of a drawing, necessary to disambiguate the drawn poses: 2D bone tangents, self-contacts, and bone foreshortening. These elements are then leveraged in an optimization inferring the 3D character pose consistent with the artist's intent. Our optimization balances cues derived from artistic literature and perception research to compensate for distorted character proportions. We demonstrate a gallery of results on sketches of numerous styles. We validate our method via numerical evaluations, user studies, and comparisons to manually posed characters and previous work. Code and data for our paper are available at http://www-labs.iro. umontreal.ca/bmpix/sketch2pose/.
S ALIENCY M APS C ONTAIN N ETWORK “F INGERPRINTS ”

Explaining deep learning models and their predictions is an open question with many proposed, but difficult to validate, solutions. This difficulty in assessing explanation methods has raised the question on the validity of these methods: What are they showing and what are the factors influencing the explanations? Furthermore, how should one choose which one to use? Here, we explore saliency-type methods, finding that saliency maps contain network “fingerprints”, by which the network which generated the map can be uniquely identified. We test this by creating datasets made up of saliency maps from different “primary” networks, then training “secondary” networks on these saliency-map datasets. We find that secondary networks can learn to identify which primary network a saliency map comes from. Our findings hold across several saliency methods and for both CNN and ResNet “primary” architectures. Our analysis also reveals complex relationships between methods: a set of methods share fingerprints, while some contain unique fingerprints. We discuss a potentially related prior work that may explain some of these relationships; some methods are made up of ’higher order derivatives’. Our simple analytical framework is a first step towards understanding ingredients of and relationships between many saliency methods.
Analysis of Fundamental Factors Affecting Stock Prices in Automotive Companies Listed on the Indonesia Stock Exchange Period 2014-2017

: This study aimed to analyse the effect of variable Return on Total Assets (ROA), Return on Equity (ROE), Price to Book Value (PBV), Debt to Equity Ratio (DER), and Earning per Share (EPS) simultaneously to the stock price of companies automotive listed on the Indonesia Stock Exchange and analyse the effect of variable Return on Total Assets (ROA), Return on Equity (ROE), Price to Book Value (PBV), Debt to Equity Ratio (DER), and Earning per Share (EPS) partially on stock prices of automotive companies listed in Indonesia stock Exchange. The number of samples in this study as many as nine companies. Mechanical analysis using multiple linear regression analysis. The analysis showed that the Return on Assets(ROA), Return on Equity (ROE), Price To Book Value (PBV), Debt to Equity Ratio (DER), Earning Per Share (EPS) simultaneously affect the stock price on the automotive company listed on the Indonesia Stock Exchange. The analysis showed that partially Price to Book Value (PBV), Debt to Equity Ratio (DER), and Earning Per Share (EPS) Return on Assets (ROA) effect on the stock price on the automotive company listed on the Indonesia Stock Exchange. Price to Book Value (PBV), and Earning Per Share (EPS) positive impact on stock prices and Debt to Equity Ratio (DER) negative impact on stock prices. While Return on Assets (ROA) and Return on Equity (ROE) has no effect on stock prices.
The effects of combined aerobic and resistance training program in Korean male youth soccer players

Background : This study analyzed the effects of short-term, off-season physical training on the core dynamic and thigh muscle function of youth soccer players. Methods : For two weeks during the off-season, middle-school soccer players (MSP, n = 75) and high-school soccer players (HSP, n = 104) participated in exercise training (five times per week). Their body composition, physical fitness, core dynamic balance, and isokinetic muscle function were compared before and after training. Results : Both groups showed significant ( p < 0.01) decreases in body composition, and significant ( p < 0.01) improvements in physical fitness (MSP, 1.02–1.15%; HSP, 1.05– 3.76%). In terms of core dynamic balance, both groups showed a significant ( p < 0.01) increase in back strength (MSP, 1.09–1.11%; HSP, 1.06–1.16%), and a significant ( p < 0.05) decrease in the left-right difference (MSP, 0.70–1.43%; HSP, 1.00–1.09%) after training. For isokinetic joint, the MSP group showed significant improvement in flexors, whereas the HSP group showed significant improvements in both the extensors and flexors. The dominant vs non-dominant deficit decreased significantly in the MSP group for total work and average power per repetition of the flexors at 60 deg/sec and in the HSP group for peak torque and total work of the flexors at 60 deg/sec. The hamstring-to-quadriceps ratio increased significantly at 60 deg/sec on the dominant side in the MSP group. Conclusions : In youth soccer players, two weeks of physical training during the off-season improved both physical fitness and core dynamic balance, suggesting that this training is effective for injury prevention.
Application of DSSAT crop simulation model to estimate the optimum dose of nitrogen fertilizer for the rice variety J-104

The rice ( Oryza sativa L.), is one of the cereals of greater production worldwide. Cuba is one of the highest consumer countries in Latin America; with values of around 72 kg per capita per year. So far, the national production only satisfies 50 % of the needs. In spite of the large amount of resources that are destined to the production of rice cultivation, the yields that are currently obtained do not satisfy the existing demand nor are economically justified. The present work was developed with the objective of applying the DSSAT crop simulation model to estimate the optimal nitrogen fertilizer dose based on the expected yield of the rice variety J-104. To calibrate the model, three experiments were evaluated in the Los Palacios Base Scientific Technological Unit, belonging to the National Institute of Agricultural Sciences (INCA), in different sowing dates. For the simulation, the runs of the model were made for different doses of nitrogen, varying them from 150 to 200 kg ha -1 , with an interval of 10 kg ha -1 and the other parameters of the model were kept constant. The results show that the model is able to describe adequately the dependence of the yields with the level of nitrogen applied and the recommended dose to obtain the best yields.
Effect of Edible Coating Containing Pomegranate Peel Extract on Quality and Shelf Life of Silver Carp (Hypophthalmichthys molitrix) Fillet during Refrigerated Storage

Effects of edible coating containing pomegranate peel extract (PPE) on the quality and shelf life of silver carp fillet during refrigerated storage were investigated. Freshly fish fillet were assigned to three treatments: control (fillet treated with edible coating and no antioxidants); fillet treated with edible coating and 5% PPE (T1) and fillet treated with edible coating and 10% PPE (T2). Chemical (pH, peroxide value and thiobarbituric acid) and microbiological (total viable count and psychrotrophic count) analysis were used to evaluated the effect of this treatments during refrigerated storage. The results show that addition of PPE considerably delayed lipid oxidation in silver carp fish fillet in T1 and T2 compared control samples. According to microbiological assay, T1 and T2 samples at days 12 reached to maximum acceptability limit while it was happened in day 9 for control samples. There were significant differences (P<0.05) between treated and control samples at all the days during the refrigerated storage.
Trends, changes and determinants of medical attention received before death among deceased adults in India: Evidence from pooled cross-sectional survey data (2004-2018)

Background: India is coping an ageing population without an adequate medical care service. However, trends, changes and factors of receiving medical attention before death remains unknown. Therefore, we intend to examine the trends, changes and determinants of medical attention received before death among the deceased adults in India. Methods: Our study has used a nationally representative pooled-cross-sectional survey data from2004 to 2017-18. We performed univariate, bivariate and multivariate analyses. We have examined the trends from 2004 to 2017-18. Then we estimated the absolute changes in medical attention rate (MAR) received before death by the 45+ deceased adults for the time period between 2004-2005 & 2017-2018 and 2014 & 2017-2018. Lastly, we applied binary logistic regression analysis to identify the effect of significant predictor variables on the medical attention received before death. Results: Our study has revealed that there has been marginal increase (0.74%) in the overall trend in medical attention received before death among 45+ adults from 2004 to 2017-18. Females, rural residence are showing negative change in receiving medical attention. However, our finding has showed that young-old, middle-old and oldest-old have significantly lower odds of receiving medical attention compared to middle-aged adults. Surprisingly, our result has revealed that Southern and Western regions are found to have significantly less likely to receive medical attention before death among deceased adults which is a striking finding. Conclusions: Therefore, there is an urgent need to establish a primary health center at community level that play an important role in order to meet the comprehensive requirements of middle-aged and older adults in India. It would be helpful to develop and execute the health policies and programs that would enable India to achieve healthy aging in India at national and subnational levels, as it is a key element of public health challenges. Keywords: medical attention received; middle-aged adults; older adults; death; pooled-cross-sectional data
When the shaking stops, the thinking starts : a grounded theory of turnover decision making in the context of a significant extra-organisational shock.

ii DEDICATION . iv ACKNOWLEDGMENTS . v TABLE OF CONTENTS . vii PRELUDE. 1 CHAPTER 1: AN INTRODUCTION . 4 INTRODUCTION . 5 WHY STUDY VOLUNTARY TURNOVER? 5 BACKGROUND TO THE RESEARCH PROBLEM. 8 PURPOSE OF THE STUDY AND RESEARCH QUESTIONS . 13 OVERVIEW OF THE RESEARCH DESIGN . 14 STRUCTURE OF THE THESIS. 16 CHAPTER 2: LITERATURE REVIEW . 18 INTRODUCTION . 19 THE PLACE OF THE LITERATURE REVIEW IN GROUNDED THEORY STUDIES . 19 DEFINING VOLUNTARY TURNOVER  The importance of understanding voluntariness. 21 TURNOVER RESEARCH IN CONTEXT The influence of conceptual tradition and dogma . 23 PREDICTING TURNOVER  In search of the holy grail . 26 Attitudinal predictors  The role of satisfaction and commitment . 29 Distal predictors: The influence of individual difference.32 Contextual predictors  The role of the work environment.33 Proximal predictors: Turnover intentions, perceived alternatives and job search behaviour . 42 Summary  the contribution of the predictive agenda.50
A Comparison of Saudi Building Code with 1997 UBC for Provisions of Modal Response Spectrum Analysis Using a Real Building

The study uses an actual building to compare the modal response spectrum analysis results of Saudi Building Code (SBC) and the 1997 Uniform Building Code (UBC) used in Saudi Arabia before the introduction of SBC. A sample of four buildings with reported analysis of comparison between IBC and UBC is taken for confirming the comparison. Eight sample places from SBC map for Saudi Arabia together with two sample places of high seismic activity in USA were taken for the comparisons. The study used software package ETABS in this study for modeling and analysis. The results are dissimilar from the comparisons reported for test places of USA. It is concluded that at most places SBC base shear is higher for both ELFP and MRSA. However, the results cannot be generalized and considered always right. The same is factual for overturning moments. Consequently, we cannot report that SBC is more conservative than UBC for all scenarios.
Methodology to extract underlying basic assumptions of a public sector construction project culture: an exploratory case study

ABSTRACT Identifying underlying basic assumptions of a construction project team brings insightfulness to cultural studies and is useful for managing project changes. However, methodological issues create barriers to such studies due to difficulties in capturing basic assumptions. This paper adopts an exploratory case study of a public sector building construction project to identify an appropriate methodology for data collection and test the ability to analyse the collected data for the purpose of extracting underlying basic assumptions of a construction project culture. Data were collected using interviews and observations, and analysed by code-based content analysis. Research findings confirm the suitability of questioning on internal integration and external adaptation problems of the construction project team to extract basic assumptions while highlighting the necessity of questioning on conflicting and critical situations of the project. Accordingly, several basic assumptions for client and consultant sub-cultural groups were identified. The methodology proposed in this study can be extended to explore basic assumptions of contractor sub-cultural group and then of the whole project.
DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain

Deep Neural Networks (DNNs) have already become a crucial computational approach to revealing the spatial patterns in the human brain; however, there are three major shortcomings in utilizing DNNs to detect the spatial patterns in functional Magnetic Resonance Signals: 1). It is a fully connected architecture that increases the complexity of network structures that is difficult to optimize and vulnerable to overfitting; 2). The requirement of large training samples results in erasing the individual/minor patterns in feature extraction; 3). The hyperparameters are required to be tuned manually, which is time-consuming. Therefore, we propose a novel deep nonlinear matrix factorization named Deep Matrix Approximately Nonlinear Decomposition (DEMAND) in this work to take advantage of the shallow linear model, e.g., Sparse Dictionary Learning (SDL) and DNNs. At first, the proposed DEMAND employs a non-fully connected and multilayer-stacked architecture that is easier to be optimized compared with canonical DNNs; furthermore, due to the efficient architecture, training DEMAND can avoid overfitting and enables the recognition of individual/minor features based on a small dataset such as an individual data; finally, a novel rank estimator technique is introduced to tune all hyperparameters of DEMAND automatically. Moreover, the proposed DEMAND is validated with four other peer methodologies via real functional Magnetic Resonance Imaging data in the human brain. In short, the validation results demonstrate that DEMAND can reveal the reproducible meta, canonical, and sub-spatial features of the human brain more efficiently than other peer methodologies.
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding. Unlike the previous efforts (e.g., non-autoregressive decoding) speeding up seq2seq generation at the cost of quality loss, our approach aims to yield the identical (or better) generation compared with autoregressive decoding but in a significant speedup, achieved by innovative cooperation of aggressive decoding and verification that are both efficient due to parallel computing. We propose two Aggressive Decoding paradigms for 2 kinds of seq2seq tasks: 1) For the seq2seq tasks whose inputs and outputs are highly similar (e.g., Grammatical Error Correction), we propose Input-guided Aggressive Decoding (IAD) that aggressively copies from the input sentence as drafted decoded tokens to verify in parallel; 2) For other general seq2seq tasks (e.g., Machine Translation), we propose Generalized Aggressive Decoding (GAD) that first employs an additional non-autoregressive decoding model for aggressive decoding and then verifies in parallel in the autoregressive manner. We test Aggressive Decoding on the most popular 6-layer Transformer model on GPU in multiple seq2seq tasks: 1) For IAD, we show that it can introduce a 7x-9x speedup for the Transformer in Grammatical Error Correction and Text Simplification tasks with the identical results as greedy decoding; 2) For GAD, we observe a 3x-5x speedup with the identical or even better quality in two important seq2seq tasks: Machine Translation and Abstractive Summarization. Moreover, Aggressive Decoding can benefit even more from stronger computing devices that are better at parallel computing. Given the lossless quality as well as significant and promising speedup, we believe Aggressive Decoding may potentially evolve into a de facto standard for efficient and lossless seq2seq generation in the near future.