Miracle-dz/UniFAR-FTUs huggingface dataset:
Director Heterogeneity and its Impact on Board Effectiveness

In the first section of the dissertation, I examine whether boards that are heterogeneous along six dimensions—age, gender, race, tenure, rank, and function—perform their most critical tasks better than boards that are more homogeneous. Using director information obtained from multiple sources and supplemented by extensive hand-collection, I estimate board heterogeneity along each of the dimensions and two aggregate measures of board heterogeneity: demographic and occupational. I find that occupationally diverse boards exhibit significantly higher CEO performance-turnover sensitivity, greater likelihood of significantly improved performance following CEO replacement, and lower excess compensation. The findings are mainly driven by tenure and rank heterogeneity. There is no evidence that any dimension of the demographic heterogeneity impacts board effectiveness in a statistically meaningful way. In the second section of the dissertation, I explore why certain dimensions of heterogeneity seem to impact board effectiveness more than others. Focusing on gender, I show that director heterogeneity improves board effectiveness for the subset of firms that committed to diversity prior to regulatory pressures, but not for the subset of firms that changed the director mix in response to external calls for diversity. This finding points to tokenism as the likely explanation for lack of impact of demographic heterogeneity on boards’ ability to act effectively. Consequently, it suggests that imposing regulatory pressures on firms to increase the level of
Imaginary Quadratic Class Groups and a Survey of Time-Lock Cryptographic Applications∗

Imaginary quadratic class groups have been proposed as one of the main hidden-order group candidates for time-lock cryptographic applications such as verifiable delay functions (VDFs). They have the advantage over RSA groups that they do not need a trusted setup. However, they have historically been significantly less studied by the cryptographic research community. This survey provides an introduction to the theory of imaginary quadratic class groups and discusses several considerations that need to be taken into account for practical applications. In particular, we describe the relevant computational problems and the main classical and quantum algorithms that can be used to solve them. From this discussion, it follows that choosing a discriminant ∆ = −p with p ≡ 3 mod 4 prime is one of the most promising ways to pick a class group Cl(∆) without the need for a trusted setup, while simultaneously making sure that there are no easy to find elements of low order in Cl(∆). We provide experimental data on class groups belonging to discriminants of this form, and compare them to the Cohen-Lenstra heuristics which predict the average behaviour of Cl(∆) belonging to a random fundamental discriminant. Afterwards, we describe the most prominent constructions of VDFs based on hidden-order groups, and discuss their soundness and sequentiality when implemented in imaginary quadratic class groups. Finally, we briefly touch upon the post-quantum security of VDFs in imaginary quadratic class groups, where the time on can use a fixed group is upper bounded by the runtime of quantum polynomial time order computation algorithms. ∗Research partially funded by NWO/TKI Grant 628.009. 014.
Feasibility study of à PLC system for avionic safety-critical systems

To increase the flexibility of the aircraft equipments and to reduce the possession and operati ng costs of the aircrafts, the main aircraft manufacturers want to change fluidic systems by electrical systems. Howev er, this evolution induces a high increase of the number of wires. Reducing the amount of wiring also allows decreasin g the construction and the maintenance costs, and the pol luting emissions. Another interest is improving the reliability of aircraft equipment such as allowing monitoring of power cables. To limit the number wires, we proposed to u se power line communication (PLC) for flight control systems (FCS) on the high voltage direct current network (HVDC), between a calculator unit and a power inverter for medium-haul aircrafts. PLC technology has proven its reliability for indoor network with the Homeplug Av standard. Nowadays, many studies deal with the possibility to use PLC f or embedded systems. However, PLC for safety-critical avionic systems are not often studied. This paper attempts o define the physical layer for such application. The propos ed transmission technique used is orthogonal frequency division multiplexing (OFDM), which is widely used with success in many telecommunication systems. In this paper, we p resent throughput measurement with Homeplug Av modems to prove the feasibility of PLC in aircraft environment. However, the Homeplug Av parameters are not adapted to the aeronautic constraints. Based on channel transf er function measurements and analysis, we proposed to adapt the OFDM parameters to comply with the FCS real-time constraints. Keywords-PLC; OFDM; coherence bandwidth; delay spread; insertion gain; channel impulse response; aircraft; avionic; bit rate; safety critical systems; HVDC network.
Human-in-the-Loop Simulation Analysis of Integrated RPAS Operations in Trajectory Based Operations Environment

In this paper, Human-in-the-Loop (HiTL) simulations of Remotely Piloted Aircraft System (RPAS) operations in two different Air Traffic Management (ATM) concepts, conventional radar vectoring and Trajectory Based Operations (TBO), were performed to assess the impacts of RPAS integration in the future ATM environment. TBO concept maximizes the throughput by planning and sharing 4-D trajectories between pilots and controllers, and it is considered one of the key concepts to enable RPASs to operate with manned aircraft in congested airspaces. RPASs are characterized by having communication delay or temporary loss of communication. TBO capability was added to the integrated air traffic simulation system for this study, which was developed in the Inha University. HiTL simulations were performed by a trainee air traffic controller with three scenarios, and the data were analyzed using safety, efficiency, and controller workload metrics. The results suggest that TBO were effective in reducing delays and controller workload while maintaining the level of safety.
Approximate Gibbs sampler for Bayesian Huberized lasso

ABSTRACT The Bayesian lasso is well-known as a Bayesian alternative for Lasso. Although the advantage of the Bayesian lasso is capable of full probabilistic uncertain quantification for parameters, the corresponding posterior distribution can be sensitive to outliers. To overcome such problem, robust Bayesian regression models have been proposed in recent years. In this paper, we consider the robust and efficient estimation for the Bayesian Huberized lasso regression in fully Bayesian perspective. A new posterior computation algorithm for the Bayesian Huberized lasso regression is proposed. The proposed approximate Gibbs sampler is based on the approximation of full conditional distribution and it is possible to estimate a tuning parameter for robustness of the pseudo-Huber loss function. Some theoretical properties of the posterior distribution are also derived. We illustrate performance of the proposed method through simulation studies and real data examples.
Separate and conquer heuristic allows robust mining of contrast sets from various types of data

Identifying diﬀerences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on a sequential covering – a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme allow generating contrast sets describing same examples with diﬀerent attributes, unlike the standard sequential covering. The ability to identify contrast sets in regression and survival data sets, the feature not provided by the existing algorithms, further extends the usability of RuleKit-CS. Experiments on wide range of data sets conﬁrmed RuleKit-CS to be a useful tool for discovering diﬀerences between deﬁned groups. The algorithm is a part of the RuleKit suite available at GitHub under GNU AGPL 3 licence (https://github.com/adaa-polsl/RuleKit).
Automatic Classification of Alzheimer's Disease using brain MRI data and deep Convolutional Neural Networks

Alzheimer's disease (AD) is one of the most common public health issues the world is facing today. This disease has a high prevalence primarily in the elderly accompanying memory loss and cognitive decline. AD detection is a challenging task which many authors have developed numerous computerized automatic diagnosis systems utilizing neuroimaging and other clinical data. MRI scans provide high-intensity visible features, making these scans the most widely used brain imaging technique. In recent years deep learning has achieved leading success in medical image analysis. But a relatively little investigation has been done to apply deep learning techniques for the brain MRI classification. This paper explores the construction of several deep learning architectures evaluated on brain MRI images and segmented images. The idea behind segmented images investigates the influence of image segmentation step on deep learning classification. The image processing presented a pipeline consisting of pre-processing to enhance the MRI scans and post-processing consisting of a segmentation method for segmenting the brain tissues. The results show that the processed images achieved a better accuracy in the binary classification of AD vs. CN (Cognitively Normal) across four different architectures. ResNet architecture resulted in the highest prediction accuracy amongst the other architectures (90.83% for the original brain images and 93.50% for the processed images).
Periodicity of power Fibonacci sequences modulus a Fibonacci number

Let F = (Fi : i ≥ 0) be the sequence of Fibonacci numbers, and j and e be non negative integers. We study the periodicity of the power Fibonacci sequences F(Fj) = (F e i (mod Fj) : i ≥ 0). It is shown that for every j, e ≥ 1 the sequence F(Fj) is periodic and its periodicity is computed. The result was previously known for F(Fj); that is, for e = 1. For e ∈ {1, 2}, the values of the normalized residues ρi ≡ F e i (mod Fj) with 0 ≤ ρi < Fj − 1 are obtained. MSC2020: 11B39 · 11B50 · 11A15
MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech

Current activity tracking technologies are largely trained on younger adults’ data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.
Unified and Effective Ensemble Knowledge Distillation

Ensemble knowledge distillation can extract knowledge from multiple teacher models and encode it into a single student model. Many existing methods learn and distill the student model on labeled data only. However, the teacher models are usually learned on the same labeled data, and their predictions have high correlations with groudtruth labels. Thus, they cannot provide sufficient knowledge complementary to task labels for student teaching. Distilling on unseen unlabeled data has the potential to enhance the knowledge transfer from the teachers to the student. In this paper, we propose a unified and effective ensemble knowledge distillation method that distills a single student model from an ensemble of teacher models on both labeled and unlabeled data. Since different teachers may have diverse prediction correctness on the same sample, on labeled data we weight the predictions of different teachers according to their correctness. In addition, we weight the distillation loss based on the overall prediction correctness of the teacher ensemble to distill high-quality knowledge. On unlabeled data, there is no groundtruth to evaluate prediction correctness. Fortunately, the disagreement among teachers is an indication of sample hardness, and thereby we weight the distillation loss based on teachers' disagreement to emphasize knowledge distillation on important samples. Extensive experiments on four datasets show the effectiveness of our proposed ensemble distillation method.
An Analytical Framework for Control Synthesis of Cyber-Physical Systems with Safety Guarantee

Cyber-physical systems (CPS) are required to operate safely under fault and malicious attacks. The simplex architecture and the recently proposed cyber resilient architectures, e.g., Byzantine fault tolerant++ (BFT++), provide safety for CPS under faults and malicious cyber attacks, respectively. However, these existing architectures make use of different timing parameters and implementations to provide safety, and are seemingly unrelated. In this paper, we propose an analytical framework to represent the simplex, BFT++ and other practical cyber resilient architectures (CRAs). We construct a hybrid system that models CPS adopting any of these architectures. We derive sufficient conditions via our proposed framework under which a control policy is guaranteed to be safe. We present an algorithm to synthesize the control policy. We validate the proposed framework using a case study on lateral control of a Boeing 747, and demonstrate that our proposed approach ensures safety of the system.
Perceptual Quality Assessment of UGC Gaming Videos

—In recent years, with the vigorous development of the video game industry, the proportion of gaming videos on major video websites like YouTube has dramatically increased. However, relatively little research has been done on the automatic quality prediction of gaming videos, especially on those that fall in the category of “User-Generated-Content” (UGC). Since current leading general-purpose Video Quality Assessment (VQA) models do not perform well on this type of gaming videos, we have created a new VQA model speciﬁcally designed to succeed on UGC gaming videos, which we call the Gaming Video Quality Predictor (GAME-VQP). GAME-VQP successfully predicts the unique statistical characteristics of gaming videos by drawing upon features designed under modiﬁed natural scene statistics models, combined with gaming speciﬁc features learned by a Convolution Neural Network. We study the performance of GAME-VQP on a very recent large UGC gaming video database called LIVE-YT-Gaming, and ﬁnd that it both outperforms other mainstream general VQA models as well as VQA models speciﬁcally designed for gaming videos. The new model will be made public after paper being accepted.
A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction

We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems. The proposed stochastic proximal point algorithm incorporates a variance reduction mechanism and the resulting SPP updates are solved using an inexact semismooth Newton framework. We establish detailed convergence results that take the inexactness of the SPP steps into account and that are in accordance with existing convergence guarantees of (proximal) stochastic variance-reduced gradient methods. Numerical experiments show that the proposed algorithm competes favorably with other state-of-the-art methods and achieves higher robustness with respect to the step size selection.
Physics Informed Shallow Machine Learning for Wind Speed Prediction

The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative data-driven approach based on supervised learning. We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in two central and north west regions of Italy (Abruzzo and Liguria). We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon). Using data from a single location and time horizon we compare systematically several algorithms where we vary the input/output variables, the memory of the input and the linear vs non-linear learning model. We then compare performance of the best algorithms across all locations and forecasting horizons. We find that the optimal design as well as its performance vary with the location. We demonstrate that the presence of a reproducible diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms and show that, when the model is accurately designed, shallow algorithms are competitive with more complex deep architectures.
Design Methodologies and CAD Tools for Leakage Power Optimization in FPGAs

The scaling of the CMOS technology has precipitated an exponential increase in both subthreshold and gate leakage currents in modern VLSI designs. Consequently, the contribution of leakage power to the total chip power dissipation for CMOS designs is increasing rapidly, which is estimated to be 40% for the current technology generations and is expected to exceed 50% by the 65nm CMOS technology. In FPGAs, the power dissipation problem is further aggravated when compared to ASIC designs because FPGA use more transistors per logic function when compared to ASIC designs. Consequently, solving the leakage power problem is pivotal to devising power-aware FPGAs in the nanometer regime. This thesis focuses on devising both architectural and CAD techniques for leakage mitigation in FPGAs. Several CAD and architectural modifications are proposed to reduce the impact of leakage power dissipation on modern FPGAs. Firstly, multi-threshold CMOS (MTCMOS) techniques are introduced to FPGAs to permanently turn OFF the unused resources of the FPGA, FPGAs are characterized with low utilization percentages that can reach 60%. Moreover, such architecture enables the dynamic shutting down of the FPGA idle parts, thus reducing the standby leakage significantly. Employing the MTCMOS technique in FPGAs requires several changes to the FPGA architecture, including the placement and routing of the sleep signals and the MTCMOS granularity. On the CAD level, the packing and placement stages are modified to allow the possibility of dynamically turning OFF the idle parts of the FPGA. A new activity generation algorithm is proposed and implemented that aims to identify the logic blocks in a design that exhibit similar idleness periods. Several criteria for the activity generation algorithm are used, including connectivity and logic function. Several versions of the activity generation algorithm are implemented to trade power savings with runtime. A newly developed packing algorithm uses the resulting activities to minimize leakage power dissipation by packing the logic blocks with similar or close activities together. By proposing an FPGA architecture that supports MTCMOS and developing a CAD tool that supports the new architecture, an average power savings of 30% is achieved for a 90nm CMOS process while incurring a speed penalty of less than 5%. This technique is further extended to provide a timing-sensitive version of the CAD flow to vary the speed penalty according to the criticality of each logic block. Secondly, a new technique for leakage power reduction in FPGAs based on the use of input dependency is developed. Both subthreshold and gate leakage power
Improving Adversarial Transferability via Neuron Attribution-based Attacks

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs beforehand in security-sensitive applications. To efficiently tackle the black-box setting where the target model's particulars are unknown, feature-level transfer-based attacks propose to contaminate the intermediate feature outputs of local models, and then directly employ the crafted adversarial samples to attack the target model. Due to the transferability of features, feature-level attacks have shown promise in synthesizing more transferable adversarial samples. However, existing feature-level attacks generally employ inaccurate neuron importance estimations, which deteriorates their transferability. To overcome such pitfalls, in this paper, we propose the Neuron Attribution-based Attack (NAA), which conducts feature-level attacks with more accurate neuron importance estimations. Specifically, we first completely attribute a model's output to each neuron in a middle layer. We then derive an approximation scheme of neuron attribution to tremendously reduce the computation overhead. Finally, we weight neurons based on their attribution results and launch feature-level attacks. Extensive experiments confirm the superiority of our approach to the state-of-the-art benchmarks. Our code is available at: hups. //rgithub.com/jprhang1810/NAA.
Scalable Whitebox Attacks on Tree-based Models

Adversarial robustness is one of the essential safety criteria for guaranteeing the reliability of machine learning models. While various adversarial robustness testing approaches were introduced in the last decade, we note that most of them are incompatible with non-differentiable models such as tree ensembles. Since tree ensembles are widely used in industry, this reveals a crucial gap between adversarial robustness research and practical applications. This paper proposes a novel whitebox adversarial robustness testing approach for tree ensemble models. Concretely, the proposed approach smooths the tree ensembles through temperature controlled sigmoid functions, which enables gradient descent-based adversarial attacks. By leveraging sampling and the log-derivative trick, the proposed approach can scale up to testing tasks that were previously unmanageable. We compare the approach against both random perturbations and blackbox approaches on multiple public datasets (and corresponding models). Our results show that the proposed method can 1) successfully reveal the adversarial vulnerability of tree ensemble models without causing computational pressure for testing and 2) flexibly balance the search performance and time complexity to meet various testing criteria.