Miracle-dz/UniFAR-FTUs huggingface dataset:
Introspection for convolutional automatic speech recognition

Artificial Neural Networks (ANNs) have experienced great success in the past few years. The increasing complexity of these models leads to less understanding about their decision processes. Therefore, introspection techniques have been proposed, mostly for images as input data. Patterns or relevant regions in images can be intuitively interpreted by a human observer. This is not the case for more complex data like speech recordings. In this work, we investigate the application of common introspection techniques from computer vision to an Automatic Speech Recognition (ASR) task. To this end, we use a model similar to image classification, which predicts letters from spectrograms. We show difficulties in applying image introspection to ASR. To tackle these problems, we propose normalized averaging of aligned inputs (NAvAI): a data-driven method to reveal learned patterns for prediction of specific classes. Our method integrates information from many data examples through local introspection techniques for Convolutional Neural Networks (CNNs). We demonstrate that our method provides better interpretability of letter-specific patterns than existing methods.
RFID Security: Techniques, Protocols and System-On-Chip Design

RFID Security: Techniques, Protocols and System-On-Chip Design is an edited book covering fundamentals, security theories and protocols, and hardware implementations for cryptography algorithms and security techniques in RFID. The volume is structured in three parts. Part 1 deals with RFID fundamentals, including system architectures and applications. Part 2 addresses RFID security protocols and techniques with a comprehensive collection of the recent state-of-art protocols and techniques to secure RFID avoiding all potential security forces and cracks. Finally, the book discusses hardware implementation of security algorithms. This section deals with the hardware implementations of cryptography algorithms and protocols dedicated to RFID platforms and chips.
Uniportal video-assisted thoracoscopic bronchial sleeve lobectomy in five patients

Objective : To investigate the clinical efficacy and value of uniportal video-assisted thoracoscopic bronchial sleeve lobectomy (BSL) in the treatment of central lung cancer. Methods : The clinical data of five patients who underwent uniportal video-assisted thoracoscopic BSL at our hospital from October 2014 to September 2016 were retrospectively analyzed. Results : The BSL procedure was successful in all five cases. The average operation time was 254 min, and the mean time to complete the anastomosis was 168 min. The average blood loss was 116 ml, and the mean number of lymph node dissections was 16.6/case. The average intubation time was 5 d, and the total postoperative drainage volume was 732 ml. The mean VAS score was 2.86, and the average postoperative hospital stay was 9.2 d. All patients were followed up for 3–19 months, and all of them survived without recurrence or metastasis. Conclusion : Uniportal video-assisted thoracoscopic BSL was found to be safe, reliable, and minimally invasive. Keywords : lung cancer; bronchial sleeve lobectomy; video-assisted thoracoscopic surgery; anastomosis
Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem

Large software organizations handle many customer support issues every day in the form of bug reports, feature requests, and general misunderstandings as submitted by customers. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, there is a chance customers will escalate their issues, and escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. This thesis provides a step towards simplifying the job for support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, a design science methodology was employed to characterize the support process and data available to IBM analysts in managing escalations. Through iterative cycles of design and evaluation, support analysts' expert knowledge about their customers was translated into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket escalations. The Machine Learning model was trained and evaluated on over 2.5 million support tickets and 10,000 escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of escalation. The features developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing the model to predict support ticket escalations, and for future researchers to build on to advance research in Escalation Prediction.
A Comprehensive Survey on Parallelization and Elasticity in Stream Processing

Stream Processing (SP) has evolved as the leading paradigm to process and gain value from the high volume of streaming data produced, e.g., in the domain of the Internet of Things. An SP system is a middleware that deploys a network of operators between data sources, such as sensors, and the consuming applications. SP systems typically face intense and highly dynamic data streams. Parallelization and elasticity enable SP systems to process these streams with continuous high quality of service. The current research landscape provides a broad spectrum of methods for parallelization and elasticity in SP. Each method makes specific assumptions and focuses on particular aspects. However, the literature lacks a comprehensive overview and categorization of the state of the art in SP parallelization and elasticity, which is necessary to consolidate the state of the research and to plan future research directions on this basis. Therefore, in this survey, we study the literature and develop a classification of current methods for both parallelization and elasticity in SP systems.
Signature-based Möller's algorithm for strong Gröbner bases over PIDs

Signature-based algorithms are the latest and most efficient approach as of today to compute Gr\"obner bases for polynomial systems over fields. Recently, possible extensions of these techniques to general rings have attracted the attention of several authors. In this paper, we present a signature-based version of M\"oller's classical variant of Buchberger's algorithm for computing strong Gr\"obner bases over Principal Ideal Domains (or PIDs). It ensures that the signatures do not decrease during the algorithm, which makes it possible to apply classical signature criteria for further optimization. In particular, with the F5 criterion, the signature version of M\"oller's algorithm computes a Gr\"obner basis without reductions to zero for a polynomial system given by a regular sequence. We also show how Buchberger's chain criterion can be implemented so as to be compatible with the signatures. We prove correctness and termination of the algorithm. Furthermore, we have written a toy implementation in Magma, allowing us to quantitatively compare the efficiency of the various criteria for eliminating S-pairs.
Temporal Overbooking of Lambda Functions in the Cloud

We consider the problem of scheduling "serverless computing" instances such as Amazon Lambda functions. Instead of a quota per tenant/customer, we assume demand for Lambda functions is modulated by token-bucket mechanisms per tenant. Based on an upper bound on the stationary number of active "Lambda servers" considering the execution-time distribution of Lambda functions, we describe an approach that the cloud could use to overbook Lambda functions for improved utilization of IT resources. An earlier bound for a single service tier is extended to the case of multiple service tiers.
Diffie-Hellman in the Air: A Link Layer Approach for In-Band Wireless Pairing

Key establishment is one fundamental issue in wireless security. The widely used Diffie-Hellman key exchange is vulnerable to the man-in-the-middle (MITM) attack due to its lack of mutual authentication. This paper presents a novel in-band solution for defending the MITM attack during the key establishment process for wireless devices. Our solution is based on the insight that an attacker inevitably affects the link layer behavior of the wireless channel, and this behavior change introduced by the attacker can be detected by legitimate users. Specifically, we propose a key exchange protocol and its corresponding channel access mechanism for the protocol message transmission, in which the Diffie-Hellman parameter is transmitted multiple times in a row without being interrupted by other data transmissions on the same channel. The proposed key exchange protocol forces the MITM attacker to cause multiple packet collisions consecutively at the receiver side, which can then be monitored by the proposed detection algorithm. The performance of the proposed solution is validated through both analysis and simulations and the results show that the proposed solution is secure against the MITM attack and can achieve a low false positive ratio. The proposed solution is in-band, and can be implemented on off-the-shelf wireless devices.
Eva-CiM: A System-Level Performance and Energy Evaluation Framework for Computing-in-Memory Architectures

Computing-in-memory (CiM) architectures aim to reduce costly data transfers by performing arithmetic and logic operations in memory and hence relieve the pressure due to the memory wall. However, determining whether a given workload can really benefit from CiM, which memory hierarchy and what device technology should be adopted by a CiM architecture requires in-depth study that is not only time consuming but also demands significant expertise in architectures and compilers. This article presents an energy and performance evaluation framework, Eva-CiM, for systems based on CiM architectures. Eva-CiM encompasses a multilevel (from device to architecture) comprehensive tool chain that leverages existing modeling and simulation tools, such as GEM5, McPAT, and DESTINY. To support high-confidence prediction, rapid design space exploration and ease of use, Eva-CiM introduces several novel modeling/analysis approaches including models for capturing memory access and dependency-aware ISA traces, and for quantifying interactions between the host CPU and the CiM module. Eva-CiM can readily produce energy and performance estimates of the entire system for a given program, a processor architecture, and the CiM array and technology specifications. Eva-CiM is validated by comparing with DESTINY. Eva-CiM enables analyses including the system-level impact of CiM-supported accesses, whether a program is CiM-favorable as well as the pros and cons of increased memory size for CiM. Eva-CiM also facilitates exploration of different design configurations and technologies.
Disentangling in Variational Autoencoders with Natural Clustering

Learning robust representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalisation in deep models. Consequently, it has been the object of much effort from the machine learning community. This work takes a step further in this direction by addressing the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We formulate a lower bound on the joint log-likelihood of inputs and class labels and present N-VAE, a model which is capable of separating factors of variation which are exclusive to certain classes from factors that are shared among classes. This model implements an explicitly compositional latent variable structure by defining a class-conditioned latent space and a shared latent space. We show its usefulness for detecting and disentangling class-dependent generative factors as well as its capacity to generate artificial samples which contain characteristics unseen in the training data.
Complex-Valued Neural Networks for Privacy Protection

This paper proposes a generic method to revise traditional neural networks for privacy protection. Our method is designed to prevent inversion attacks, i.e., avoiding recovering private information from intermediate-layer features of a neural network. Our method transforms real-valued features of an intermediate layer into complex-valued features, in which private information is hidden in a random phase of the transformed features. To prevent the adversary from recovering the phase, we adopt an adversarial-learning algorithm to generate the complex-valued feature. More crucially, the transformed feature can be directly processed by the deep neural network, but without knowing the true phase, people cannot recover either the input information or the prediction result. Preliminary experiments with various neural networks (including the LeNet, the VGG, and residual networks) on different datasets have shown that our method can successfully defend feature inversion attacks while preserving learning accuracy.
Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs

Many causal processes in biomedicine contain cycles and evolve. However, most causal discovery algorithms assume that the underlying causal process follows a single directed acyclic graph (DAG) that does not change over time. The algorithms can therefore infer erroneous causal relations with high confidence when run on real biomedical data. In this paper, I relax the single DAG assumption by modeling causal processes using a mixture of DAGs so that the graph can change over time. I then describe a causal discovery algorithm called Causal Inference over Mixtures (CIM) to infer causal structure from a mixture of DAGs using longitudinal data. CIM improves the accuracy of causal discovery on both real and synthetic clinical datasets even when cycles, non-stationarity, non-linearity, latent variables and selection bias exist simultaneously.
Software Architecture Metrics: a literature review

In Software Engineering, early detection of architectural issues is key. It helps mitigate the risk of poor performance, and lowers the cost of repairing these issues. Metrics give a quick overview of the project which helps designers with the detection of flaws or degradation in their architecture. Even though studies unveiled architectural metrics more than 25 years ago, they have not yet been embraced by the industry nor the open source community. In this study, we aim at conducting a review of existing metrics focused on the software architecture for evaluating quality, early in the design flow and throughout the project's lifetime. We also give guidelines of their usage and study their relevance in different contexts.
Derandomized Load Balancing using Random Walks on Expander Graphs

In a computing center with a huge amount of machines, when a job arrives, a dispatcher need to decide which machine to route this job to based on limited information. A classical method, called the power-of-$d$ choices algorithm is to pick $d$ servers independently at random and dispatch the job to the least loaded server among the $d$ servers. In this paper, we analyze a low-randomness variant of this dispatching scheme, where $d$ queues are sampled through $d$ independent non-backtracking random walks on a $k$-regular graph $G$. Under certain assumptions of the graph $G$ we show that under this scheme, the dynamics of the queuing system converges to the same deterministic ordinary differential equation (ODE) for the power-of-$d$ choices scheme. We also show that the system is stable under the proposed scheme, and the stationary distribution of the system converges to the fixed point of the ODE.
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding

We present ErasureHead, a new approach for distributed gradient descent (GD) that mitigates system delays by employing approximate gradient coding. Gradient coded distributed GD uses redundancy to exactly recover the gradient at each iteration from a subset of compute nodes. ErasureHead instead uses approximate gradient codes to recover an inexact gradient at each iteration, but with higher delay tolerance. Unlike prior work on gradient coding, we provide a performance analysis that combines both delay and convergence guarantees. We establish that down to a small noise floor, ErasureHead converges as quickly as distributed GD and has faster overall runtime under a probabilistic delay model. We conduct extensive experiments on real world datasets and distributed clusters and demonstrate that our method can lead to significant speedups over both standard and gradient coded GD.
Quality measures for SAR images

Synthetic aperture radar (SAR) contains information features different from those of infrared images and conventional photography. Image quality measures specifically created for SAR are necessary to measure SAR processor performance especially in the context of lossy image data compression. The quality measures discussed include several existing measures and newly developed measures created to better gauge specific features of SAR imagery. Each quality performance measure is evaluated subjectively to determine the applicability to SAR image fidelity. <<ETX>>
"And We Will Fight For Our Race!" A Measurement Study of Genetic Testing Conversations on Reddit and 4chan

Progress in genomics has enabled the emergence of a booming market for “direct-to-consumer” genetic testing. Nowadays, companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. At the same time, alt- and far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic “purity. ” In this paper, we present a measurement study shedding light on how genetic testing is being discussed on Web communities in Reddit and 4chan. We collect 1.3M comments posted over 27 months on the two platforms, using a set of 280 keywords related to genetic testing. We then use NLP and computer vision tools to identify trends, themes, and topics of discussion. Our analysis shows that genetic testing attracts a lot of attention on Reddit and 4chan, with discussions often including highly toxic language expressed through hateful, racist, and misogynistic comments. In particular, on 4chan's politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly antisemitic rhetoric, often conveyed through memes. Finally, we find that discussions build around user groups, from technology enthusiasts to communities promoting fringe political views.
Out-of-Sample Testing for GANs

We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a test set to directly measure the reconstruction quality in the original sample space (no auxiliary networks are necessary), and it also computes the (log)likelihood for the reconstructed samples in the test set. Further, EvalGAN is agnostic to the GAN algorithm and the dataset. We decided to test it on three state-of-the-art GANs over the well-known CIFAR-10 and CelebA datasets.
Streamlines for Motion Planning in Underwater Currents

Motion planning for underwater vehicles must consider the effect of ocean currents. We present an efficient method to compute reachability and cost between sample points in sampling-based motion planning that supports long-range planning over hundreds of kilometres in complicated flows. The idea is to search a reduced space of control inputs that consists of stream functions whose level sets, or streamlines, optimally connect two given points. Such stream functions are generated by superimposing a control input onto the underlying current flow. A streamline represents the resulting path that a vehicle would follow as it is carried along by the current given that control input. We provide rigorous analysis that shows how our method avoids exhaustive search of the control space, and demonstrate simulated examples in complicated flows including a traversal along the east coast of Australia, using actual current predictions, between Sydney and Brisbane.
Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate

Stochastic approximation (SA) is a classical approach for stochastic convex optimization. Previous studies have demonstrated that the convergence rate of SA can be improved by introducing either smoothness or strong convexity condition. In this paper, we make use of smoothness and strong convexity simultaneously to boost the convergence rate. Let $\lambda$ be the modulus of strong convexity, $\kappa$ be the condition number, $F_*$ be the minimal risk, and $\alpha>1$ be some small constant. First, we demonstrate that, in expectation, an $O(1/[\lambda T^\alpha] + \kappa F_*/T)$ risk bound is attainable when $T = \Omega(\kappa^\alpha)$. Thus, when $F_*$ is small, the convergence rate could be faster than $O(1/[\lambda T])$ and approaches $O(1/[\lambda T^\alpha])$ in the ideal case. Second, to further benefit from small risk, we show that, in expectation, an $O(1/2^{T/\kappa}+F_*)$ risk bound is achievable. Thus, the excess risk reduces exponentially until reaching $O(F_*)$, and if $F_*=0$, we obtain a global linear convergence. Finally, we emphasize that our proof is constructive and each risk bound is equipped with an efficient stochastic algorithm attaining that bound.
Making Deep Q-learning methods robust to time discretization

Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key to making DRL applicable to real world problems. In this paper, we identify sensitivity to time discretization in near continuous-time environments as a critical factor; this covers, e.g., changing the number of frames per second, or the action frequency of the controller. Empirically, we find that Q-learning-based approaches such as Deep Q- learning (Mnih et al., 2015) and Deep Deterministic Policy Gradient (Lillicrap et al., 2015) collapse with small time steps. Formally, we prove that Q-learning does not exist in continuous time. We detail a principled way to build an off-policy RL algorithm that yields similar performances over a wide range of time discretizations, and confirm this robustness empirically.
CoCoNet: A Collaborative Convolutional Network

We present an end-to-end CNN architecture for fine-grained visual recognition called Collaborative Convolutional Network (CoCoNet). The network uses a collaborative filter after the convolutional layers to represent an image as an optimal weighted collaboration of features learned from training samples as a whole rather than one at a time. This gives CoCoNet more power to encode the fine-grained nature of the data with limited samples in an end-to-end fashion. We perform a detailed study of the performance with 1-stage and 2-stage transfer learning and different configurations with benchmark architectures like AlexNet and VggNet. The ablation study shows that the proposed method outperforms its constituent parts considerably and consistently. CoCoNet also outperforms the baseline popular deep learning based fine-grained recognition method, namely Bilinear-CNN (BCNN) with statistical significance. Experiments have been performed on the fine-grained species recognition problem, but the method is general enough to be applied to other similar tasks. Lastly, we also introduce a new public dataset for fine-grained species recognition, that of Indian endemic birds and have reported initial results on it. The training metadata and new dataset are available through the corresponding author.