Miracle-dz/UniFAR-FTUs huggingface dataset:
Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability

Voting plays a central role in bringing crowd wisdom to collective decision making, meanwhile data privacy has been a common ethical/legal issue in eliciting preferences from individuals. This work studies the problem of aggregating individual's voting data under the local differential privacy setting, where usefulness and soundness of the aggregated scores are of major concern. One naive approach to the problem is adding Laplace random noises, however, it makes aggregated scores extremely fragile to new types of strategic behaviors tailored to the local privacy setting: data amplification attack and view disguise attack. The data amplification attack means an attacker's manipulation power is amplified by the privacy-preserving procedure when contributing a fraud vote. The view disguise attack happens when an attacker could disguise malicious data as valid private views to manipulate the voting result. In this work, after theoretically quantifying the estimation error bound and the manipulating risk bound of the Laplace mechanism, we propose two mechanisms improving the usefulness and soundness simultaneously: the weighted sampling mechanism and the additive mechanism. The former one interprets the score vector as probabilistic data. Compared to the Laplace mechanism for Borda voting rule with $d$ candidates, it reduces the mean squared error bound by half and lowers the maximum magnitude risk bound from $+\infty$ to $O(\frac{d^3}{n\epsilon})$. The latter one randomly outputs a subset of candidates according to their total scores. Its mean squared error bound is optimized from $O(\frac{d^5}{n\epsilon^2})$ to $O(\frac{d^4}{n\epsilon^2})$, and its maximum magnitude risk bound is reduced to $O(\frac{d^2}{n\epsilon})$. Experimental results validate that our proposed approaches averagely reduce estimation error by $50\%$ and are more robust to adversarial attacks.
Numerical convergence of hydrodynamical simulations of galaxy formation: the abundance and internal structure of galaxies and their cold dark matter haloes

We address the issue of numerical convergence in cosmological smoothed particle hydrodynamics simulations using a suite of runs drawn from the eagle project. Our simulations adopt subgrid models that produce realistic galaxy populations at a fiducial mass and force resolution, but systematically vary the latter in order to study their impact on galaxy properties. We provide several analytic criteria that help guide the selection of gravitational softening for hydrodynamical simulations, and present results from runs that both adhere to and deviate from them. Unlike dark matter-only simulations, hydrodynamical simulations exhibit a strong sensitivity to gravitational softening, and care must be taken when selecting numerical parameters. Our results – which focus mainly on star formation histories, galaxy stellar mass functions and sizes – illuminate three main considerations. First, softening imposes a minimum resolved escape speed, vϵ, due to the binding energy between gas particles. Runs that adopt such small softening lengths that $v_\epsilon \gtrsim 10\, {\rm km\, s^{-1}}$ (the sound speed in ionized ${\sim }10^4\, {\rm K}$ gas) suffer from reduced effects of photoheating. Secondly, feedback from stars or active galactic nuclei may suffer from numerical overcooling if the gravitational softening length is chosen below a critical value, ϵeFB. Thirdly, we note that small softening lengths exacerbate the segregation of stars and dark matter particles in halo centres, often leading to the counterintuitive result that galaxy sizes increase as softening is reduced. The structure of dark matter haloes in hydrodynamical runs respond to softening in a way that reflects the sensitivity of their galaxy populations to numerical parameters.
Tomographic analyses of the CMB lensing and galaxy clustering to probe the linear structure growth

In a tomographic approach, we measure the cross-correlation between the CMB lensing reconstructed from the Planck satellite and the galaxies of the photometric redshift catalogue based on the combination of the South Galactic Cap u-band Sky Survey (SCUSS), Sloan Digital Sky Survey (SDSS), and Wide-field Infrared Survey Explorer (WISE) data. We perform the analyses considering six redshift bins spanning the range of 0.1 <z<0.7. From the estimates of the galaxy-galaxy and galaxy-CMB lensing power spectrum, we derive the galaxy bias and the amplitude of the cross-correlation for each redshift bin. We have finally applied these tomographic measurements to estimate the linear structure growth using the bias-independent D̂G estimator introduced by [1]. We find that the amplitude of the structure growth with respect to the fiducial cosmology is AD=1.16±0.13, closely consistent with the predictions of the ΛCDM model (ADΛ CDM=1). We perform several tests for consistency of our results, finding no significant evidence for systematic effects.
Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews

Aspect and opinion terms extraction from review texts is one of the key tasks in aspect-based sentiment analysis. In order to extract aspect and opinion terms for Indonesian hotel reviews, we adapt double embeddings feature and attention mechanism that outperform the best system at SemEval 2015 and 2016. We conduct experiments using 4000 reviews to find the best configuration and show the influences of double embeddings and attention mechanism toward model performance. Using 1000 reviews for evaluation. , we achieved Fl-measure of 0.914 and 0.90 for aspect and opinion terms extraction in token and entity (term) level respectively.
A Simple Software Rejuvenation Framework Based on Model Driven Development

In the current dynamic-natured business environment, it is inevitable that today’s software systems may not be suitable for tomorrow’s business challenges which indicate that the software in use has aged. Although we cannot prevent software aging, we can try to prolong the aging process of software so that it can be used for longer. In this paper, we outline a conceptual software rejuvenation framework based on model driven development approach. The framework is simple but effective and can be implemented in a recursive five step process. We have illustrated the applicability of the framework using a simple business case study which highlights the effectiveness of the framework. This work adds to the existing literature on software aging and its preventative measures. It also fills in the research gap which exists about software aging caused by changing requirements. Index Terms: Model Driven Development, Software Aging, Software Rejuvenation Framework
Probabilistic Program Neurogenesis

We present a new method for addressing the challenge of continual learning wherein an agent must adapt to new tasks while maintaining high performance on previously learned tasks. To accomplish this, an agent must identify previously acquired information that generalizes to the new task while also adapting its internal model to learn information that is specific to the new task. Our approach is based on neurogenesis, which involves adding new neurons to a previously trained neural network in an intelligent way. To our knowledge, we are the first to leverage probabilistic programming within the framework of evolutionary computation to optimize the growth of neural networks for continual learning. Through a series of experiments, we show that our approach is able to consistently find better performing solutions than genetic algorithms and it is able to do so faster.
Investigation on Performance of Neural Networks Using Quadratic Relative Error Cost Function

The performance of neural networks with quadratic cost function (MSE cost function) is analyzed in terms of the adjustment rate of weights and its performance in multi-magnitude data processing using a qualitative mathematical method based on mean squared error. However, neural networks using quadratic cost functions exhibit low-weight updating rates and variations in performances in multi-magnitude data processing. This paper investigates the performance of neural networks using a quadratic relative error cost function (REMSE cost function). Two-node-to-one-node models are built to investigate the performance of the REMSE and MSE cost functions in adjustment rate of weights and multi-magnitude data processing. A three-layer neural network is employed to compare the training and prediction performances of the REMSE cost function and MSE cost function. Three LSTM networks are used to evaluate the differences between REMSE, MSE, and Logcosh in actual applications by learning stress and strain of soil. The results indicate that the REMSE cost function can notably accelerate the adjustment rate of weights and improve the performance of the neural network in small magnitude data regression. The applications of the REMSE cost function are also discussed.
Confounding factors analysis and compensation for high-speed bearing diagnostics

In recent years, machine diagnostics through vibration monitoring is gaining a rising interest. Indeed, in the literature many advanced techniques are available to disclose the fault establishment as well as damage type, location and severity. Unfortunately, in general, these high-level algorithms are not robust to operational and environmental variables, restricting the field of applicability of machine diagnostics. Most of industrial machines in fact, work with variable loads, at variable speeds and in uncontrolled environments, so that the finally measured signals are often non-stationary. The very common time-series features based on statistical moments (such as root mean square, skewness, kurtosis, peak value and crest factor) undergo variations related to changes in the machine operational parameters (e.g. speed, load, .) or in the environmental parameters (e.g. temperature, humidity, .), which can be seen as non-measured, and then latent, confounding factors with respect to the health information of interest. In order to face such issue, statistical techniques like (in a first exploratory stage) the Principal Component Analysis, or the Factor Analysis, are available. The pursuit of features insensitive to these factors, can be also tackled exploiting the cointegration property of non-stationary signals. In this paper, the most common methods for reducing the influence of latent factors are considered, and applied to investigate the data collected over the rig available @DIRG Lab, specifically conceived to test high speed aeronautical bearings monitoring vibrations by means of 2 tri-axial accelerometers while controlling the rotational speed (6000 – 30000 RPM), the radial load (0 to 1800 N) and recording the lubricant oil temperature. The compensation scheme is based on two procedures which are established in univariate analyses, but not so well documented in multivariate cases: the removal of deterministic trends by subtraction of a regression, and the removal of stochastic trends in difference stationary series by subtraction of the one-step ahead prediction from an autoregressive model. The extension of these methods to the multivariate case is here analysed to find an effective way of enhancing damage patterns when the masking effect due to the non-stationarities induced by latent factors is strong.
AVPark: Reservation and Cost Optimization-Based Cyber-Physical System for Long-Range Autonomous Valet Parking (L-AVP)

The autonomous vehicle (AV) is an emerging product of intelligent transportation system. This paper proposes a new parking cost optimization scheme for long-range autonomous valet parking (L-AVP), namely AVPark. The L-AVP selects a drop-off point (as the temporary reference point for people to fetch the AV for traveling purpose) for AV. The user leaves AV at drop-off spot and the AV finds out the most optimal car parks (CPs) itself. The AVPark provides an AV with the most optimal car park considering the parking price, fuel consumption, and distance to a vacant parking space. AVPark aims to minimize the walking distance for drivers, and also the round-trip duration for AV from the drop-off point to car park through a combination of weighted values and heuristic approach. By facilitating the drop-off point that is newly brought into the emerging scenario, an optimization scheme is proposed to minimize the total cost for fuel consumption and traveling time using the weighted value analysis. The results show that AVPark optimized the total trip duration, walking distance, and cost.
Collective Change Detection: Adaptivity to Dynamic Swarm Densities and Light Conditions in Robot Swarms

Robot swarms are known to be robust to individual robot failures. However, a reduced swarm size causes a reduced swarm density. A too low swarm density may then decrease swarm performance, that should be compensated by adapting the individual behavior. Similarly, swarm behaviors can also be adapted to changes in the environment, such as dynamic light conditions. We study aggregation of swarm robots controlled by an extended variant of the BEECLUST algorithm. The robots are asked to aggregate at the brightest spot in their environment. Our approach efficiently adapts this swarm aggregation behavior to variability in swarm density and light conditions. First, each robot individually monitors its environment continuously by sampling its local swarm density and perceived light condition. Second, we exploit the collaboration of robots by letting them share features of these measurements with their neighbors by communication. In extensive robot swarm experiments with ten robots we validate our approach with dynamically changing swarm densities and under dynamic light conditions. We find an improved performance compared to robot swarms without communication and without awareness of the swarm density.
A Compact Resistor-Based Temperature Sensor

Smart temperature sensors can be used for the temperature compensation of micro-electromechanical (MEMS) frequency references. From the different types of CMOS temperature sensors, resistor-based sensors are currently the most energy-efficient. However, compared with bipolar transistor (BJT)-based temperature sensors, resistors suffer from higher nonlinearity and spread of their temperature dependencies which require multi-point calibration to achieve similar accuracy. Given that a new design direction should attempt to combine the best features of both types of sensors, the target of this work is to develop a resistor-based temperature sensor that should be more energy-efficient than BJT-based sensors, but with a competitive area.This thesis investigates circuit techniques to obtain a design of a compact resistor-based temperature sensor (area l 0.1mm2) which is energy-efficient,while achieving high resolution ( sub - mK) and high accuracy (inaccuracyl 0.1°C). To achieve the target specifications, a frequency-locked-loop (FLL) is implemented around the sensing element. Design directions were presented,consisting of a continuous-time readout and a discrete-time readout together with their limitations and performance. Based on the simulation results, it can be stated that the combination between the FLL readout and the Wien-bridge sensing element enables the design of a compact sensor which achieves the target specifications.
Fast parallel image reconstruction for cone‐beam FDK algorithm

FDK algorithm is a popular analytical reconstruction method for practical cone‐beam CT scanners. Compared with iterative methods, the FDK algorithm is computationally efficient. However, the reconstruction speed remains a limitation for its application when dealing with high resolution images. In this paper, we propose a fast method for parallel implementation of the FDK algorithm by the use of multi‐GPU. First, we optimize the backprojection operation of FDK according to the property of geometric symmetry and the correlation between adjacent slices. Then, we utilize the multi‐thread technology to realize the parallel implementation of the optimized FDK algorithm on multi‐GPU. Finally, we implement the proposed method on a multi‐GPU platform. Numerical experiment shows that the proposed multi‐GPU‐based approach can reconstruct a 512 cubed volume in 1.9 seconds from 360 projections of resolution 512 ×512, which is 511 times faster than a traditional CPU–based approach and 5 times faster than a single GPU–based approach. In addition, the reconstruction results also indicate that the proposed method can maintain the same precision with traditional method.
Assessing the Relative Importance of an E-learning system’s Usability Design Characteristics Based on Students' Preferences

This study considers the interaction between an e-learning system, the Blackboard system, and the students who use it in Saudi Arabia. While previous work exists, there is limited consideration of the assessment of the preferences of e-learning system usability variables based on students’ perspectives, especially in developing countries such as Saudi Arabia. This paper attempts to fill the gap by investigating the relative importance of the design criteria developed for e-learning system usability evaluation from the students’ perspective in Saudi tertiary education. Based on reviewed literature, a set of usability principles was developed that have had an influence in the students’ learning process and use of the e-learning system. The list includes system navigation, system learnability, visual design, information quality, instructional assessment and system interactivity. An exploratory study was carried out to identify the most important usability design characteristics from a student’s perspective and then evaluate the overall usability of the current e-learning system, based on this subset. A quantitative approach was adopted to weigh usability design characteristics, based on 181 learners’ perceptions. The sample consists of undergraduates who are users of a web-based e-learning system in a university in Saudi Arabia. The research instrument was tested for construct validity and reliability. The analysed results have shown that information quality is the most important dimension followed by the navigation of the e-learning system. The study has also revealed that the system learnability and visual design came third and fourth in order of importance of e-learning system usability assessment. Finally, the least important design categories that influenced the e-learning system usability assessment were instructional assessment and system interactivity. The empirical results of this study may help to provide insights for designers and evaluators leading to a more effective approach to improve the usability and uptake of the e-learning system.
Using determinism to predict garden paths

I am interested in making a psychologically valid model of human natural language understanding, and especially in a processing model for predicting when a sentence will be a garden path. While extending the Marcus deterministic parser to include noun-noun modification, several counter examples to Marcus' garden path prediction were found. In this paper I propose that when people encounter an ambigous situation that may lead to a garden path, they use semantics to decide rather than look ahead. I will present an extension to the garden path prediction mechanism of Marcus' parser to account for this and several experiments to test this theory.
Knowledge Distillation for Machine Translation

: Encoder-to-Decoder is a newly architecture for Neural Machine Translation (NMT). Convolutional Neural Network (CNN) based on this framework has gained significant success in NMT task. Challenges remain in the practical use of CNN model, which is in need of bilingual sentence pairs for training and each bilingual data is designed for CNN translation model needing retraining. Although some successful performance has been reported, it is an important research direction to avoid model overfitting caused by the scarcity of parallel corpus. The paper introduces a simple and efficient knowledge distillation method for regularization to solve CNN training overfitting problems by transferring the knowledge of source model to adapted model on low-resource languages in NMT task. The experiment on English-Czech dataset result shows that our model solve the over fitting problem, get better generalization, and improve the performance of a low-resource languages translation task.
Design and Analysis of a Disc Brake Rotor for Optimal Performance in Racing

The braking system is an important and indispensable part of an automotive. The brake disc rotor forms part of the braking system and plays a significant role to effectively stop the vehicle. Therefore, the investigation of rotor design and analysis is important towards attaining optimal braking performance. In this research, three designs of vented rotor geometry were tested, namely normal vented, vented and cross-drilled and vented and slotted, and subjected to coupled thermal-structural analyses using ANSYS Workbench. For material selection for these designs, the CES EduPack software was applied and Carbon/Carbon (C/C) composite was identified as the best material for use as a rotor material for racing application. The results showed that the vented and cross-drilled design is the most appropriate design for the vehicle applications since it records lowest maximum temperature, stress, strain and total deformation compared to the other two design. This design also helps to enhance brake performance by allowing for faster heat dissipation and hence reducing thermal stresses on the rotor.
Forecasting stock trends through Machine Learning

In managing their portfolio of investments, managers are constantly seeking the instruments (e.g. stocks) that consistently provide the highest returns on a long-term horizon. One possibility to improve the search for overperforming stocks is to use predictive Machine Learning techniques. Previous studies attempted to predict alpha, i.e. higher than average returns, by developing linear models based on capital market anomalies. However, the techniques used in these studies lack the capability to capture complex non-linear dependencies. More recently, evidence suggests that some Machine Learning techniques may be able to capture these non-linearities [1], namely Long Short-Term Memory (LSTM) networks. The LSTM networks belong to the class of Recurrent Neural Networks (RNN) and are used to capture patterns in sequential data. Despite the recent technological advances in Deep Learning in the last years, little research has been done to analyze and identify long-term investment opportunities using predictive Deep Learning techniques. Most available research focuses primarily on short-term predictions and only makes use of market data, disregarding other publicly available information, such as fundamentals. Using the financial statements and market prices of companies listed in the NASDAQ Stock Exchange and the New York Stock Exchange (NYSE) over a span of 30 years, different features (e.g. Book-to-Market ratio, Return On Equity), and different Machine Learning techniques were tested, namely the LSTM neural network. As a baseline, the buy-and-hold investment strategy was used, which comprised of buying a stock at the beginning of the time series, without ever selling. All of the developed models were be tested by simulating trading on an out-of-sample period. The models were then be compared using the Accuracy, Year-over-Year Returns, and Maximum Drawdown for the period tested. The models tested proved to achieve Accuracy measurements higher than the Naive Classifier. Moreover, the results suggest that the LSTM neural network is not the most suitable learning algorithm for this context, achieving worse results than Random Forests and Logistic Regression (with or without the use of Bagging). Although the portfolios built using the Classifier’s predictions were more profitable than the benchmarks, the increased Drawdown indicates a necessity for adequate portfolio risk optimization and risk management.