Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands

We study deep neural networks and their use in semiparametric inference. We establish novel nonasymptotic high probability bounds for deep feedforward neural nets. These deliver rates of convergence that are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second‐step inference after first‐step estimation with deep learning, a result also new to the literature. Our nonasymptotic high probability bounds, and the subsequent semiparametric inference, treat the current standard architecture: fully connected feedforward neural networks (multilayer perceptrons), with the now‐common rectified linear unit activation function, unbounded weights, and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed‐width, very deep networks. We establish the nonasymptotic bounds for these deep nets for a general class of nonparametric regression‐type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focusing on causal parameters for concreteness, and demonstrate the effectiveness of deep learning with an empirical application to direct mail marketing.
Firm Characteristics and Expected Stock Returns

We analyze the joint out-of-sample predictive ability of a comprehensive set of 299 firm characteristics for cross-sectional stock returns. We develop a cross-sectional out-of-sample R2 statistic that provides an informative measure of the accuracy of cross-sectional return forecasts in terms of mean squared forecast error. To improve cross-sectional return forecasts based on a large number of firm characteristics, we propose an E-LASSO approach that implements shrinkage in a flexible manner. Our new approach produces significant cross-sectional out-of-sample R2 gains on a consistent basis over time and provides the most accurate out-of-sample estimates of cross-sectional expected returns to date. The E-LASSO approach also generates substantial economic value in the context of long-short portfolios. Finally, we present evidence that more characteristics work better than fewer with respect to forecasting cross-sectional stock returns.
Interference management in large-scale MIMO systems for 5G

The thesis is part of the prospect of the explosion of data traffic generated by the increase of the number of users as well as the growth of the bit rate which must be taken into account in the definition of future generations of radio-cellular communications. A solution is the large-scale MIMO technology (MIMO systems oflarge size) which poses several challenges. The design of the new low complexity detection algorithms is indispensable since the conventional algorithms are no longer adapted to this configuration because of their poor detection performance or their too high complexity depending on the number of antennas. A first contribution of the thesis is an algorithm based on the technique of compressed sensing by exploiting the propertiesof the signals with finite alphabet. Applied to large-scale, determined and under-determined MIMO systems, this algorithm achieves promising and superior performance (quality ofdetection, complexity) compared to state-ofthe-art algorithms. A thorough theoretical study was conducted to determine the optimal operating conditions and the statistical distribution of outputs. A second contribution is the integration of the original algorithm into an iterative receiver by differentiating the coded and uncoded cases. Another challenge to keeping the promise of large- scale MIMO systems (high spectral efficiency) is channel estimation. A third contribution of the thesis is the proposal of semi-blind channel estimation algorithms that work with a minimum size of pilot sequences (equal to the number of users) and reach performances very close to the theoretical bound.
Investigation of the mechanism of transport across the poly/monocrystalline silicon interface in polysilicon-emitter bipolar transistors based on variations in the interface treatment process

Electrical transport across the poly/monocrystalline silicon (poly-Si/c-Si) interface has been investigated by introducing variations in the interface treatment process affecting the thickness of the native oxide layer formed on the silicon surface. The I-V relationship characterizing the resistance associated with this interface is extracted using an improved open-collector method, which eliminates the inherent nonlinearity arising from the reverse active characteristics of the bipolar transistor. The nonohmic behavior of the interface at low electric fields is demonstrated to be consistent with direct tunneling through a rectangular barrier presented by an ultrathin interfacial oxide layer. An approximate tunnel junction model is proposed that fits the tunneling current in the low bias regime, predicting the experimentally observed increase in the low-voltage leakage current associated with ultrathin oxides. The thickness of the interfacial oxide extracted by fitting the measured data to the proposed rectangular-barrier tunnel junction model is in good agreement with reported experimental values. Finally, a relation between experimentally introduced variations in the interface treatment process and the magnitude of the resistance associated with the interface is identified, which can be quantitatively explained based on the direct tunnel junction model in terms of differences in the native oxide thickness.
Multi-Criteria Evaluation and Benchmarking for Young Learners’ English Language Mobile Applications in Terms of LSRW Skills

This study proposes an evaluation and benchmarking decision matrix (DM) on the basis of multi-criteria decision making (MCDM) for young learners’ English mobile applications (E-apps) in terms of listening, speaking, reading and writing (LSRW) skills. Benchmarking E-apps for young learners is challenging due to (a) multiple criteria, (b) criteria importance and (c) data variation. The DM was constructed on the basis of the intersection amongst evaluation criteria in terms of LSRW and E-apps for young learners. The criteria were adopted from a preschool education curriculum standard. The DM data included six E-apps as alternatives and 17 skills as criteria. Thereafter, the six E-apps were evaluated by distributing a checklist form amongst six English learning experts. These apps were subsequently benchmarked by utilising MCDM methods, namely, best–worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS). BWM was used for criterion weighting, whereas TOPSIS was employed to benchmark and rank the apps. TOPSIS was utilised in two contexts, namely, individual and group. In the group context, internal and external aggregations are applied. Mean was computed to ensure that the E-apps undergo a systematic ranking for objective validation. This study provides scenarios and a benchmarking checklist to evaluate and compare the proposed work with six relative studies. Results indicated that (1) BWM is suitable for criteria weighting. (2) TOPSIS is suitable for benchmarking and ranking E-apps. Moreover, the internal and external TOPSIS group decision making exhibited similar findings, with the best app being ‘Montessori’ and the worst app being ‘FunWithFlupe. ’ (3) For objective validation, remarkable differences were observed amongst the group scores, which indicate that the internal and external ranking results are identical. (4) In the evaluation, the proposed DM revealed advantages over the six relative studies by 40.00%, 53.33%, 40.00%, 46.67%, 46.67% and 46.67%.
Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids

The extensive deployment of smart meters in millions of households provides a huge amount of individual electricity consumption data for demand side analysis at a fine granularity. Different from traditional aggregated system-level data, smart meter data is more irregular and unpredictable. As a result, probabilistic load forecasting (PLF), which can provide a better understanding of the uncertainty and volatility in future demand, is critical to constructing energy-efficient and reliable smart grids. In this article, a recently developed technique called Bayesian deep learning is employed to solve this challenging problem. In particular, a novel multitask PLF framework based on Bayesian deep learning is proposed to quantify the shared uncertainties across distinct customer groups while accounting for their differences. Further, a clustering-based pooling method is designed to increase the data diversity and volume for the framework. This not only addresses the problem of overfitting but also improves the predictive performance. Numerical results are presented which demonstrate that the proposed framework provides superior probabilistic forecasting accuracy over conventional methods.
The use of deep learning to solve invariance issues in object recognition

Object recognition is a complex problem in the field of computer vision and artificial intelligence with many different solutions including the use of Neural Networks and Deep Learning. This thesis investigates the use of Deep learning for object recognition and improvements to the deep learning methodology using methods such as dropout to reduce overfitting. Improvements to the dropout method are made using selective neuron dropping instead of random neuron dropping used in traditional uniform dropout to improve results up to 1.17% on the CHARS74K dataset using neuron output variance to select neurons for dropping. This thesis also explores the use of Deep Convolutional Neural Networks to create a rotation invariant neural network by using parallel convolutional layers with tied weights with filters presented at different rotations, combining results using a winner takes all pooling method to produce rotation invariance. Results of this method showed great improvement over benchmarks on rotated test data showing a 52.32% increase in accuracy on the MNIST dataset and a 36.44% accuracy increase on the CHARS74K dataset. This work was furthered by attempting to introduce a scaling method into the filter rotation to reduce data loss and blurring by the rotation method, however it was found that results using this method worsened the accuracy of the network compared to benchmarks not using the method, showing a decrease in accuracy of 0.17% on the MNIST dataset and a decrease of 3.68% on the CHARS74K dataset.
SieveDroid: Intercepting Undesirable Private-Data Transmissions in Android Applications

Android applications may abuse their permissions on private data, leaking the user's privacy out of the mobile device via data transmissions. A straightforward way to deal with this problem is to restrict the application permissions. However, the restriction not only prevents the undesirable operations from leaking privacy but also prohibits the desirable operations from utilizing private data. Our goal is to get both of the points: ensuring the user's privacy by preventing undesirable private-data transmissions and enduring no degradation on the desirable application functionalities. We propose SieveDroid, a fine-grained runtime privacy control framework. SieveDroid addresses two key challenges of intercepting the undesirable private-data transmissions: revealing how private-data transmissions occur to end-users and setting privacy control rules which intercept the undesirable private-data transmissions at runtime without interfering with desirable ones. We evaluate SieveDroid by using Android malware samples and real applications from the Android market. The results demonstrate that SieveDroid reveals the undesirable operations precisely and concisely and it can effectively intercept the undesirable data transmissions. Besides, desirable functionalities in 98% of the tested applications are not affected.
Remote interpreter API model for supporting computer programming adaptive learning

Despite the adoption of Learning Management Systems (LMS) has been continuously growing in the last decade few, if any, scholars addressed the lack of the interactivity in online learning systems. The requirement for an interactive learning model has been increasingly prominent as content providers realize the importance of personalization of content suits to learners’ learning progress. This paper demonstrates the adoption of adaptive learning into existing LMS engine to overcome the limitation of either systems plugins or server specification pertinent to students’ psychomotor abilities in computer programming learning. In this regard, the psychomotor is understood as the ability of students to write the program code as correctly as expected. In this study, a web API model that works to run an interpreter based-program code remotely was developed. The web API model can be utilized by LMS so it becomes the solution to the problem. The structure of the web API model has been adapted to the needs of the learning assessment. The implementation of the developed API web model is done in Python and PHP programming languages. The performance test was done by submitting 10 to 100 program codes simultaneously indicated no significant difference to the required resources (CPU usage and memory usage) to run the program code. Furthermore, for response time, the average time needed to run Python and PHP program code is also no significant difference. The average of CPU usage required by the web API to run a Python program code is 0.2058% with 0.5973 seconds as a response time. Meanwhile, to run the PHP program code, the average CPU usage required is 0.8074% with 0.3110 seconds response time. It can be concluded that the web API performance does not overburden the server.
Research project BRIK: development of an innovative method for determining
the precise trajectory of a railway vehicle

In the paper the essential assumptions regarding a research project implemented by a consortium of Gdansk University of Technology and Gdynia Maritime University are presented. The project has been commissioned by National Center of Research and Development with cooperation with Polish Railways (PKP Polskie Linie Kolejowe S.A.). The project is focused in implementation of modern measurement techniques using Global Navigation Positioning System GNSS, Inertial Navigation System INS and Mobile Laser Scanning MLS in a railway network management process. The precise aim of the research is both determine geometric parameters of a railway geometric layout as well as a railway vehicle movement trajectory. For this reason the innovative technique of mobile satellite measurements will be used. In the paper a synthetic description of the project together with the particular stages are presented. Also, a part of research which was realized in 2018 is shown. In the first stage, Gdansk University of Technology made a research of existing common methods for determining and assessing a track alignment, presented an idea of mobile investigation of track axis by the use of satellite measurements, took into consideration an issue of influence of dynamic behavior of the measurement vehicle on the accuracy of determined track positions by the use of GNSS/INS techniques and finally presented a technical project of measurement platform. Gdynia Maritime University made a query of GNSS/INS/MLS in measurements of track alignment, elaborated a scheme of measurement devices placement on the mobile measurement platform and studied possibilities of increasing accuracy for estimation a railway vehicle localization in relation to common techniques used by polish train operators.
Task Data Offloading and Resource Allocation in Fog Computing With Multi-Task Delay Guarantee

With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-user’s limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.
Determination of appropriate compression rate for developing the Persian version of time compressed sentence test

Background and Aim: Time compressed spe­ech test is one of the most useful monaural tests for evaluation of central auditory processing disorder. For developing the time compressed sentences test, the compression rate of the sen­tences must be set so that the average speech comprehension score is about 90% in normal individuals and can challenge central auditory processing system sufficiently so subjects with auditory processing disorders could be identi­fied. Therefore, the aim of the present study was finding the appropriate compression rate for developing compressed sentences test in Persian. Method: Initially, two 10-sentence lists were prepared based on the experts’ opinion and were compressed by the amount of 60, 65, 70, 75 and 80% using Praat software. Compressed sentences were tested on twelve 18–25 year-old normal individuals and the speech comprehen­sion score in different compression rates was compared and the compression rate in which the average score was approximately 90% was reported as an appropriate compression rate for developing time compressed sentences test in Persian. Results: 70% compression rate was able to cre­ate an average score of 95.27% (± 3.31) and 93.6% (± 7.17) in the right and left ear, res­pectively. Conclusion: Results showed that the compre­ssion rate of 70% was appropriate rate for dev­eloping the test in the Persian language.
Oil Forecasting Using Artificial Intelligence

The motivation for this research paper is the application of two novel models in the prediction of crude oil index. The first model is a generic deep belief network and the second model is an adaptive neural fuzzy inference system. Furthermore we have to emphasize on the second contribution in this paper which is the use of an extensive number of inputs including mixed and autoregressive inputs. Both proposed methodologies have been used in the past in different problems such as face recognition, prediction of chromosome anomalies etch, providing higher outputs than usual. For comparison purposes, the forecasting statistical and empirical accuracy of models is benchmarked with traditional strategies such as a naive strategy, a moving average convergence divergence model and an autoregressive moving average model. As it turns out, the proposed novel techniques produce higher statistical and empirical results outperforming the other linear models. Concluding first time such research work brings such outstanding outputs in terms of forecasting oil markets.
Techno-economic Viability and Energy Conversion Analysis of RHES with Less Weight/Area

This article proposes a new strategy to find the optimal location, configuration and size of the Renewable (wind-photovoltaic-diesel-battery) Hybrid Energy Systems (RHES (off-grid)). This study has two steps: first, the proposaltoa strategybased on a weather change to find the optimal location in Iraq using Hybrid Optimization Model for Electric-Renewables (HOMER) software. Second, the study will examine the influence of the techno-economic viability from side less weight and area on the optimal configuration/size of the RHES, which gives the maximum output power. A period of one-year for meteorological data for both solar radiation and wind speed has used. Finally, simulation results indicated that the optimal location for this RHES is the AL Harithah location. The analysis has shown that RHES can supply 89% of the load demands by renewable-energy. It is also successful in reducing the area required for installation of the RHES about 28%.
Food Non-Destructive Quality Evaluation Using Color Image Analysis System

Quality inspection of food and agricultural product are difficult and labor-intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in Cameroon are manual which is costly as well as unreliable because the human decision in identifying quality factors such as appearance, flavor, nutrient, texture, is inconsistent, subjective and slow. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has an important place in the food industry. The application of color image analysis system, in this case, shows clearly that all the food product studied here were discriminated over 95%.
Going Mobile: The Effects of Smartphone Usage on Internet Consumption

With relatively small screens and limited display, smartphones significantly affect users' online browsing experience relative to fixed devices like the desktop. As consumers increasingly access the Internet through mobile devices, this paper explores the effects of a shift towards smartphone Internet access on the consumption of online content. Using data on the clickstream activity of over 2,900 individuals on both their smartphone and desktop, I estimate the effect of smartphone usage on users' allocation of time across various categories of websites, as well as their diversity and depth of online content consumption. Employing an instrumental variables approach based on updates of the smartphone operating system, the results show an increase in the usage of game and social networking domains at the expense of news and shopping domains - among others - as mobile usage increases relative to desktop. I also find that the diversity of consumption decreases within several categories, whereas consumption depth increases for games and social networking categories and decreases for search and news domains. Results show limited differences across consumer demographics. These results have important implications for website publishers, advertisers, and online competition.