Reviewerly/exHarmony huggingface dataset:
Securing the Human

Recent global demand for cybersecurity professionals is promising, with the U.S. job growth rate at 28%, three times the national average. Lacking qualified applicants, many organizations struggle to fill open positions. In a global survey, 2,300 security managers reported that 59% of their security positions were unfilled, although 82% anticipated cyberattacks to their systems. At the same time, the cybersecurity field is broadening, not only in technical concepts but also in human factors, business processes, and international law. The field has not become culturally diversified, however. Professionals hired in 2018 included only 24.9% women, 12.3% African Americans, and 6.8% Latinos. These facts create an opportunity for higher education: diversify the profession while increasing the numbers of skilled computer scientists. New and integrated methods of attracting student populations in the field of cybersecurity are needed. The working group goal is to evaluate the effectiveness of approaches used in higher education to diversify the cybersecurity field through literature review, analysis of the findings, and a survey on techniques used for diversification of the cybersecurity field.
Single reference image based scene relighting via material guided filtering

Image relighting is to change the illumination of an image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the input image. Then, the reference image is warped to the input image through patch match based image warping. Lastly, the input image is relit using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference image on the input image using only a single reference image.
DEEP-FRI: Sampling outside the box improves soundness

Motivated by the quest for scalable and succinct zero knowledge arguments, we revisit worst-case-to-average-case reductions for linear spaces, raised by [Rothblum, Vadhan, Wigderson, STOC 2013]. The previous state of the art by [Ben-Sasson, Kopparty, Saraf, CCC 2018] showed that if some member of an affine space U is δ-far in relative Hamming distance from a linear code V - this is the worst-case assumption - then most elements of U are almost-δ-far from V - this is the average case. However, this result was known to hold only below the double function of the relative distance δ_V of the code V, i.e., only when δ < 1-(1-δ_V)^(1/4).First, we increase the soundness-bound to the function of δ_V and show that the average distance of U from V is nearly δ for any worst-case distance δ smaller than 1-(1-δ_V)^(1/3). This bound is tight, which is somewhat surprising because the one-and-a-half Johnson function is unfamiliar in the literature on error correcting codes.To improve soundness further for Reed Solomon codes we sample outside the box. We suggest a new protocol in which the verifier samples a single point z outside the box D on which codewords are evaluated, and asks the prover for the value at z of the interpolating polynomial of a random element of U. Intuitively, the answer provided by the prover forces it to choose one codeword from a list of pretenders that are close to U. We call this technique Domain Extending for Eliminating Pretenders (DEEP).The DEEP method improves the soundness of the worst-case-to-average-case reduction for RS codes up their list decoding radius. This radius is bounded from below by the Johnson bound, implying average distance is approximately δ for all δ < 1-(1-δ_V)^(1/2). Under a plausible conjecture about the list decoding radius of Reed-Solomon codes, average distance from V is approximately δ for all δ. The DEEP technique can be generalized to all linear codes, giving improved reductions for capacity-achieving list-decodable codes.Finally, we use the DEEP technique to devise two new protocols: - An Interactive Oracle Proof of Proximity (IOPP) for RS codes, called DEEP-FRI. The soundness of the protocol improves upon that of the FRI protocol of [Ben-Sasson et al., ICALP 2018] while retaining linear arithmetic proving complexity and logarithmic verifier arithmetic complexity. - An Interactive Oracle Proof (IOP) for the Algebraic Linking IOP (ALI) protocol used to construct zero knowledge scalable transparent arguments of knowledge (ZK-STARKs) in [Ben-Sasson et al., eprint 2018]. The new protocol, called DEEP-ALI, improves soundness of this crucial step from a small constant < 1/8 to a constant arbitrarily close to 1.
Revisiting Adversarial Risk

Recent works on adversarial perturbations show that there is an inherent trade-off between standard test accuracy and adversarial accuracy. Specifically, they show that no classifier can simultaneously be robust to adversarial perturbations and achieve high standard test accuracy. However, this is contrary to the standard notion that on tasks such as image classification, humans are robust classifiers with low error rate. In this work, we show that the main reason behind this confusion is the inaccurate definition of adversarial perturbation that is used in the literature. To fix this issue, we propose a slight, yet important modification to the existing definition of adversarial perturbation. Based on the modified definition, we show that there is no trade-off between adversarial and standard accuracies; there exist classifiers that are robust and achieve high standard accuracy. We further study several properties of this new definition of adversarial risk and its relation to the existing definition.
lfda: Local Fisher Discriminant Analysis in R

Fisher discriminant analysis (Scholkopft & Mullert, 1999) is a popular choice to reduce the dimensionality of the original dataset.It maximizes between-class scatter and minimizes within-class scatter.It works really well in practice but lacks some considerations for multimodality.Multimodality exists within many applications, such as disease diagnosis, where there may be multiple causes for a particular disease.In this situation, Fisher discriminant analysis cannot capture the multimodal characteristics of the clusters.To deal with multimodality, local-preserving projection (Niyogi, 2004) preserves the local structure of the data in that it keeps nearby data pairs in the original data space close in the embedding space.As a result, multimodal data could be embedded and its local structure will not be lost.
Designing Convolutional Neural Networks Using a Genetic Approach for Ball Detection

At RoboCup 2017, the HULKs reached the Standard Platform League’s quarter finals and won the mixed team competition together with our fellow team B-Human. This paper describes the design of a convolutional neural network used for the detection of the black and white ball - one of the key contributions that led to the team’s success. We present a genetic design approach that optimizes network hyperparameters for a cost effective inference on the NAO, with limited amount of training data. Experimental results demonstrate that the genetic algorithm is able to optimize the hyperparameters of convolutional neural networks. We show that the resulting network is able to run in real-time on the robot with a very precise classification in generalization test.
From high-level inference algorithms to efficient code

Probabilistic programming languages are valuable because they allow domain experts to express probabilistic models and inference algorithms without worrying about irrelevant details. However, for decades there remained an important and popular class of probabilistic inference algorithms whose efficient implementation required manual low-level coding that is tedious and error-prone. They are algorithms whose idiomatic expression requires random array variables that are latent or whose likelihood is conjugate . Although that is how practitioners communicate and compose these algorithms on paper, executing such expressions requires eliminating the latent variables and recognizing the conjugacy by symbolic mathematics. Moreover, matching the performance of handwritten code requires speeding up loops by more than a constant factor. We show how probabilistic programs that directly and concisely express these desired inference algorithms can be compiled while maintaining efficiency. We introduce new transformations that turn high-level probabilistic programs with arrays into pure loop code. We then make great use of domain-specific invariants and norms to optimize the code, and to specialize and JIT-compile the code per execution. The resulting performance is competitive with manual implementations.
Learning Representations in Reinforcement Learning

Author(s): Rafati Heravi, Jacob | Advisor(s): Noelle, David C. | Abstract: Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection policy to increase rewarding experiences in their environments. Temporal Difference (TD) learning algorithm, a model-free RL method, attempts to find an optimal policy through learning the values of agent's actions at any state by computing the expected future rewards without having access to a model of the environment. TD algorithms have been very successful on a broad range of control tasks, but learning can become intractably slow as the state space grows. This has motivated methods for using parameterized function approximation for the value function and developing methods for learning internal representations of the agent's state, to effectively reduce the size of state space and restructure state representations in order to support generalization. This dissertation investigates biologically inspired techniques for learning useful state representations in RL, as well as optimization methods for improving learning. There are three parts to this investigation. First, failures of deep RL algorithms to solve some relatively simple control problems are explored. Taking inspiration from the sparse codes produced by lateral inhibition in the brain, this dissertation offers a method for learning sparse state representations. Second, the challenges of RL in efficient exploration of environments with sparse delayed reward feedback, as well as the scalability issues in large-scale applications are addressed. The hierarchical structure of motor control in the brain prompts the consideration of approaches to learning action selection policies at multiple levels of temporal abstraction. That is learning to select subgoals separately from action selection policies that achieve those subgoals. This dissertation offers a novel model-free Hierarchical Reinforcement Learning framework, including approaches to automatic subgoal discovery based on unsupervised learning over memories of past experiences. Third, more complex optimization methods than those typically used in deep learning, and deep RL are explored, focusing on improving learning while avoiding the need to fine tune many hyperparameters. This dissertation offers limited-memory quasi-Newton optimization methods to efficiently solve highly nonlinear and nonconvex optimization problems for deep learning and deep RL applications. Together, these three contributions provide a foundation for scaling RL to more complex control problems through the learning of improved internal representations.
Improving Fairness in a Large Scale HTC System Through Workload Analysis and Simulation

Monitoring and analyzing the execution of a workload is at the core of the operation of data centers. It allows operators to verify that the operational objectives are satisfied or detect and react to any unexpected and unwanted behavior. However, the scale and complexity of large workloads composed of millions of jobs executed each month on several thousands of cores, often limit the depth of such an analysis. This may lead to overlook some phenomena that, while not harmful at a global scale, can be detrimental to a specific class of users. In this paper, we illustrate such a situation by analyzing a large High Throughput Computing (HTC) workload trace coming from one of the largest academic computing centers in France. The Fair-Share algorithm at the core of the batch scheduler ensures that all user groups are fairly provided with an amount of computing resources commensurate to their expressed needs. However, a deeper analysis of the produced schedule, especially of the job waiting times, shows a certain degree of unfairness between user groups. We identify the configuration of the quotas and scheduling queues as the main root causes of this unfairness. We thus propose a drastic reconfiguration of the system that aims at being more suited to the characteristics of the workload and at better balancing the waiting time among user groups. We evaluate the impact of this reconfiguration through detailed simulations. The obtained results show that it still satisfies the main operational objectives while significantly improving the quality of service experienced by formerly unfavored users.
Lazy open quantum walks

Open quantum walks (OQWs) describe a quantum walker on an underlying graph whose dynamics is purely driven by dissipation and decoherence. Mathematically, they are formulated as completely positive trace preserving (CPTP) maps on the space of density matrices for the walker on the graph. Any microscopically derived OQW must include the possibility of remaining on the same site on the graph when the map is applied. We extend the CPTP map to describe a lazy OQW. We derive a central limit theorem for lazy OQWs on a $d$-dimensional lattice, where the distribution converges to a Gaussian. We show that the properties of this Gaussian computed using conventional methods agree with the general formulas derived from our central limit theorem.
Exploring Hybrid CTC/Attention End-to-End Speech Recognition with Gaussian Processes

Hybrid CTC/attention end-to-end speech recognition combines two powerful concepts. Given a speech feature sequence, the attention mechanism directly outputs a sequence of letters. Connectionist Temporal Classification (CTC) helps to bind the attention mechanism to sequential alignments. This hybrid architecture also gives more degrees of freedom in choosing parameter configurations. We applied Gaussian process optimization to estimate the impact of network parameters and language model weight in decoding towards Character Error Rate (CER), as well as attention accuracy. In total, we trained 70 hybrid CTC/attention networks and performed 590 beam search runs with an RNNLM as language model on the TEDlium v2 test set. To our surprise, the results challenge the assumption that CTC primarily regularizes the attention mechanism. We argue in an evidence-based manner that CTC instead regularizes the impact of language model feedback in a one-pass beam search, as letter hypotheses are fed back into the attention mechanism. Attention-only models without RNNLM already achieved $$10.9\%$$ CER, or $$22.4\%$$ Word Error Rate (WER), on the TEDlium v2 test set. Combined decoding of same attention-only networks with RNNLM strongly underperformed, with at best $$40.2\%$$ CER, or, $$49.3\%$$ WER. A combined hybrid CTC/attention model with RNNLM performed best, with $$8.9\%$$ CER, or $$17.6\%$$ WER.
Benchmarking Sampling-based Probabilistic Object Detectors.

This paper provides the first benchmark for sampling-based probabilistic object detectors. A probabilistic objectdetector expresses uncertainty for all detections that reli-ably indicates object localisation and classification perfor-mance. We compare performance for two sampling-baseduncertainty techniques, namely Monte Carlo Dropout andDeep Ensembles, when implemented into one-stage andtwo-stage object detectors, Single Shot MultiBox Detectorand Faster R-CNN. Our results show that Deep Ensemblesoutperform MC Dropout for both types of detectors. We alsointroduce a new merging strategy for sampling-based tech-niques and one-stage object detectors. We show this novelmerging strategy has competitive performance with previ-ously established strategies, while only having one free pa-rameter
Extending OpenMP Metadirective Semantics for Runtime Adaptation

OpenMP 5.0 introduces the metadirective to support selection from a set of directive variants based on the OpenMP context, which is composed of traits from active OpenMP constructs, devices, implementations or user-defined conditions. OpenMP 5.0 restricts the selection to be determined at compile time, which requires that all traits must be compile-time constants. Our analysis of real applications indicates that this restriction has its limitation, and we explore extension of user-defined contexts to support variant selection at runtime. We use the Smith-Waterman algorithm as an example to show the need for adaptive selection of parallelism and devices at runtime, and present a prototype implemented in the ROSE compiler. Given a large range of input sizes, our experiments demonstrate that one of the adaptive versions of Smith-Waterman always chooses the parallelism and device that delivers the best performance, with improvements between 20% and 200% compared to non-adaptive versions that use the other approaches.
Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations

Speech acts are the actions that a speaker intends when performing an utterance within conversations. In this paper, we proposed speech act classification for asynchronous conversations on Twitter using multiple machine learning methods including SVM and deep neural networks. We applied the proposed methods on the ArSAS tweets dataset. The obtained results show that superiority of deep learning methods compared to SVMs, where Bi-LSTM managed to achieve an accuracy of 87.5% and a macro-averaged F1 score 61.5%. We believe that our results are the first to be reported on the task of speech-act recognition for asynchronous conversations on Arabic Twitter.
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning

Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives. We derive an objective that, under automatic differentiation, produces low-variance unbiased estimators of derivatives at any order. Our objective is compatible with arbitrary advantage estimators, which allows the control of the bias and variance of any-order derivatives when using function approximation. Furthermore, we propose a method to trade off bias and variance of higher order derivatives by discounting the impact of more distant causal dependencies. We demonstrate the correctness and utility of our estimator in analytically tractable MDPs and in meta-reinforcement-learning for continuous control.
Importance Weighted Hierarchical Variational Inference

Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior distribution. In turn, the expressivity of the variational family is largely limited by the requirement of having a tractable density function. To overcome this roadblock, we introduce a new family of variational upper bounds on a marginal log-density in the case of hierarchical models (also known as latent variable models). We then derive a family of increasingly tighter variational lower bounds on the otherwise intractable standard evidence lower bound for hierarchical variational distributions, enabling the use of more expressive approximate posteriors. We show that previously known methods, such as Hierarchical Variational Models, Semi-Implicit Variational Inference and Doubly Semi-Implicit Variational Inference can be seen as special cases of the proposed approach, and empirically demonstrate superior performance of the proposed method in a set of experiments.
On New Zero-Knowledge Proofs for Lattice-Based Group Signatures with Verifier-Local Revocation

For lattice-based group signatures with verifier-local revocation (VLR), a group member who issues a signature on behalf of the whole group can validly prove to the verifiers with an efficient non-interactive zero-knowledge proof protocol, from which the verifiers only come to the conclusion that the signer is a certified group member who owns a valid secret signing key and its corresponding revocation token is out of the revocation list. The first such construction was introduced by Langlois et al. (PKC 2014), furthermore, a full and corrected version was proposed in TCS 2018. However, both schemes are within the structure of Bonsai Trees, and thus the bit-sizes of the group public-key and the group member secret-key are proportional to $$\log N$$ , where N is the maximum number of group members, therefore both constructions are not suitable for a large group. In this work, we adopt a more efficient and compact identity-encoding technique which only needs a constant number of matrices to encode the group member’s identity information and it saves a $$\mathcal {O}(\log N)$$ factor in both bit-sizes for the group public-key and the group member secret-key. In particular, a new Stern-type statistical zero-knowledge proof protocol allowing to prove the signer’s validity as a valid certified group member and its revocation token correctly committed via a one-way and injective Learning With Errors ( $$\textsf {LWE}$$ ) function is proposed.
Learning Distributions Generated by One-Layer ReLU Networks

We consider the problem of estimating the parameters of a $d$-dimensional rectified Gaussian distribution from i.i.d. samples. A rectified Gaussian distribution is defined by passing a standard Gaussian distribution through a one-layer ReLU neural network. We give a simple algorithm to estimate the parameters (i.e., the weight matrix and bias vector of the ReLU neural network) up to an error $\eps\norm{W}_F$ using $\widetilde{O}(1/\eps^2)$ samples and $\widetilde{O}(d^2/\eps^2)$ time (log factors are ignored for simplicity). This implies that we can estimate the distribution up to $\eps$ in total variation distance using $\widetilde{O}(\kappa^2d^2/\eps^2)$ samples, where $\kappa$ is the condition number of the covariance matrix. Our only assumption is that the bias vector is non-negative. Without this non-negativity assumption, we show that estimating the bias vector within any error requires the number of samples at least exponential in the infinity norm of the bias vector. Our algorithm is based on the key observation that vector norms and pairwise angles can be estimated separately. We use a recent result on learning from truncated samples. We also prove two sample complexity lower bounds: $\Omega(1/\eps^2)$ samples are required to estimate the parameters up to error $\eps$, while $\Omega(d/\eps^2)$ samples are necessary to estimate the distribution up to $\eps$ in total variation distance. The first lower bound implies that our algorithm is optimal for parameter estimation. Finally, we show an interesting connection between learning a two-layer generative model and non-negative matrix factorization. Experimental results are provided to support our analysis.