Reviewerly/exHarmony huggingface dataset:
Discrepancy, Coresets, and Sketches in Machine Learning

This paper defines the notion of class discrepancy for families of functions. It shows that low discrepancy classes admit small offline and streaming coresets. We provide general techniques for bounding the class discrepancy of machine learning problems. As corollaries of the general technique we bound the discrepancy (and therefore coreset complexity) of logistic regression, sigmoid activation loss, matrix covariance, kernel density and any analytic function of the dot product or the squared distance. Our results prove the existence of epsilon-approximation O(sqrt{d}/epsilon) sized coresets for the above problems. This resolves the long-standing open problem regarding the coreset complexity of Gaussian kernel density estimation. We provide two more related but independent results. First, an exponential improvement of the widely used merge-and-reduce trick which gives improved streaming sketches for any low discrepancy problem. Second, an extremely simple deterministic algorithm for finding low discrepancy sequences (and therefore coresets) for any positive semi-definite kernel. This paper establishes some explicit connections between class discrepancy, coreset complexity, learnability, and streaming algorithms.
Scale Trotter: Illustrative Visual Travels Across Negative Scales

We present ScaleTrotter, a conceptual framework for an interactive, multi-scale visualization of biological mesoscale data and, specifically, genome data. ScaleTrotter allows viewers to smoothly transition from the nucleus of a cell to the atomistic composition of the DNA, while bridging several orders of magnitude in scale. The challenges in creating an interactive visualization of genome data are fundamentally different in several ways from those in other domains like astronomy that require a multi-scale representation as well. First, genome data has intertwined scale levels-the DNA is an extremely long, connected molecule that manifests itself at all scale levels. Second, elements of the DNA do not disappear as one zooms out-instead the scale levels at which they are observed group these elements differently. Third, we have detailed information and thus geometry for the entire dataset and for all scale levels, posing a challenge for interactive visual exploration. Finally, the conceptual scale levels for genome data are close in scale space, requiring us to find ways to visually embed a smaller scale into a coarser one. We address these challenges by creating a new multi-scale visualization concept. We use a scale-dependent camera model that controls the visual embedding of the scales into their respective parents, the rendering of a subset of the scale hierarchy, and the location, size, and scope of the view. In traversing the scales, ScaleTrotter is roaming between 2D and 3D visual representations that are depicted in integrated visuals. We discuss, specifically, how this form of multi-scale visualization follows from the specific characteristics of the genome data and describe its implementation. Finally, we discuss the implications of our work to the general illustrative depiction of multi-scale data.
Exploiting Related and Unrelated Tasks for Hierarchical Metric Learning and Image Classification

In multi-task learning, multiple interrelated tasks are jointly learned to achieve better performance. In many cases, if we can identify which tasks are related, we can also clearly identify which tasks are unrelated. In the past, most researchers emphasized exploiting correlations among interrelated tasks while completely ignoring the unrelated tasks that may provide valuable prior knowledge for multi-task learning. In this paper, a new approach is developed to hierarchically learn a tree of multi-task metrics by leveraging prior knowledge about both the related tasks and unrelated tasks. First, a visual tree is constructed to hierarchically organize large numbers of image categories in a coarse-to-fine fashion. Over the visual tree, a multi-task metric classifier is learned for each node by exploiting both the related and unrelated tasks, where the learning tasks for training the classifiers for the sibling child nodes under the same parent node are treated as the interrelated tasks, and the others are treated as the unrelated tasks. In addition, the node-specific metric for the parent node is propagated to its sibling child nodes to control inter-level error propagation. Our experimental results demonstrate that our hierarchical metric learning algorithm achieves better results than other state-of-the-art algorithms.
A Study of Deep Learning for Network Traffic Data Forecasting

We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance, especially in large networks. In a nutshell, we wish to predict, in advance, the bit rate for a transmission, based on low-dimensional connection metadata ("flows") that is available whenever a communication is initiated. Our study has several genuinely new points: First, it is performed on a large dataset (~50 million flows), which requires a new training scheme that operates on successive blocks of data since the whole dataset is too large for in-memory processing. Additionally, we are the first to propose and perform a more fine-grained prediction that distinguishes between low, medium and high bit rates instead of just "mice" and "elephant" flows. Lastly, we apply state-of-the-art visualization and clustering techniques to flow data and show that visualizations are insightful despite the heterogeneous and non-metric nature of the data. We developed a processing pipeline to handle the highly non-trivial acquisition process and allow for proper data preprocessing to be able to apply DNNs to network traffic data. We conduct DNN hyper-parameter optimization as well as feature selection experiments, which clearly show that fine-grained network traffic forecasting is feasible, and that domain-dependent data enrichment and augmentation strategies can improve results. An outlook about the fundamental challenges presented by network traffic analysis (high data throughput, unbalanced and dynamic classes, changing statistics, outlier detection) concludes the article.
Power weighted shortest paths for clustering Euclidean data

We study the use of power weighted shortest path metrics for clustering high dimensional Euclidean data, under the assumption that the data is drawn from a collection of disjoint low dimensional manifolds. We argue, theoretically and experimentally, that this leads to higher clustering accuracy. We also present a fast algorithm for computing these distances.
Adaptive and Dynamically Constrained Process Noise Estimation for Orbit Determination

This paper introduces two new algorithms to accurately estimate the process noise covariance of a discrete-time Kalman filter online for robust orbit determination in the presence of dynamics model uncertainties. Common orbit determination process noise techniques, such as state noise compensation and dynamic model compensation, require offline tuning and a priori knowledge of the dynamical environment. Alternatively, the process noise covariance can be estimated through adaptive filtering. However, many adaptive filtering techniques are not applicable to onboard orbit determination due to computational cost or the assumption of a linear time-invariant system. Furthermore, existing adaptive filtering techniques do not constrain the process noise covariance according to the underlying continuous-time dynamical model, and there has been limited work on adaptive filtering with colored process noise. To overcome these limitations, a novel approach is developed which optimally fuses state noise compensation and dynamic model compensation with covariance matching adaptive filtering. This yields two adaptive and dynamically constrained process noise covariance estimation techniques. Unlike many adaptive filtering approaches, the new techniques accurately extrapolate over measurement outages and do not rely on ad hoc methods to ensure the process noise covariance is positive semi-definite. The benefits of the proposed algorithms are demonstrated through two case studies: an illustrative linear system and the autonomous navigation of two spacecraft orbiting an asteroid.
An Overview of Blockchain for Higher Education

Blockchain technology is one of the most widely acclaimed technologies of recent years. It enables the creation of distributed applications involving multiple actors or organizations in which transactions and data are not under the control of a central authority. Transactions are stored in a distributed public ledger in an immutable format in such a way that they can be verified by participants. This disintermediation promises to remove much of the “friction” (and lower costs) from distributed transactions by cutting out the intermediary party, thus enabling a variety of applications in finance, government, health, etc. This paper provides an overview of recent research in the application of blockchain technology to higher education for those who are interested in working in this area or just understanding how higher education may be positively impacted in the future by blockchain.
Using AI to Attack VA: A Stealthy Spyware Against Voice Assistances in Smart Phones

Voice Assistants (VAs) are increasingly popular for human-computer interaction (HCI) smartphones. To help users automatically conduct various tasks, these tools usually come with high privileges and are able to access sensitive system resources. A comprised VA is a stepping stone for attackers to hack into users’ phones. Prior work has experimentally demonstrated that VAs can be a promising attack point for HCI tools. However, the state-of-the-art approaches require ad-hoc mechanisms to activate VAs that are non-trivial to trigger in practice and are usually limited to specific mobile platforms. To mitigate the limitations faced by the state-of-the-art, we propose a novel attack approach, namely <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vaspy</monospace> , which crafts the users’ “activation voice” by silently listening to users’ phone calls. Once the activation voice is formed, Vaspy can select a suitable occasion to launch an attack. Vaspy embodies a machine learning model that learns suitable attacking times to prevent the attack from being noticed by the user. We implement a proof-of-concept spyware and test it on a range of popular Android phones. The experimental results demonstrate that this approach can silently craft the activation voice of the users and launch attacks. In the wrong hands, a technique like Vaspy can enable automated attacks to HCI tools. By raising awareness, we urge the community and manufacturers to revisit the risks of VAs and subsequently revise the activation logic to be resilient to the style of attacks proposed in this work.
Blink: Fast and Generic Collectives for Distributed ML

Model parameter synchronization across GPUs introduces high overheads for data-parallel training at scale. Existing parameter synchronization protocols cannot effectively leverage available network resources in the face of ever increasing hardware heterogeneity. To address this, we propose Blink, a collective communication library that dynamically generates optimal communication primitives by packing spanning trees. We propose techniques to minimize the number of trees generated and extend Blink to leverage heterogeneous communication channels for faster data transfers. Evaluations show that compared to the state-of-the-art (NCCL), Blink can achieve up to 8× faster model synchronization, and reduce end-to-end training time for image classification tasks by up to 40%.
Structural Analysis of Sparse Neural Networks

Sparse Neural Networks regained attention due to their potential of mathematical and computational advantages. We give motivation to study Artificial Neural Networks (ANNs) from a network science perspective, provide a technique to embed arbitrary Directed Acyclic Graphs into ANNs and report study results on predicting the performance of image classifiers based on the structural properties of the networks' underlying graph. Results could further progress neuroevolution and add explanations for the success of distinct architectures from a structural perspective.
Mediator-assisted cooling in quantum annealing

We show a significant reduction of errors for an architecture of quantum annealers where bosonic modes mediate the interaction between qubits. These systems have a large redundancy in the subspace of solutions, supported by arbitrarily large bosonic occupations. We explain how this redundancy leads to a mitigation of errors when the bosonic modes operate in the ultrastrong coupling regime. Numerical simulations also predict a large increase of qubit coherence for a specific annealing problem with mediated interactions. We provide evidence that noise reduction occurs in more general types of quantum computers with similar architectures.
MicroSCOPE: Enabling Access Control in Searchable Encryption with the Use of Attribute-Based Encryption and SGX

Secure cloud storage is considered as one of the most important problems that both businesses and end-users take into account before moving their private data to the cloud. Lately, we have seen some interesting approaches that are based either on the promising concept of Symmetric Searchable Encryption (SSE) or on the well-studied field of Attribute-Based Encryption (ABE). Our construction, MicroSCOPE, combines both ABE and SSE to utilize the advantages of each technique. Finally, we enhance our construction with an access control mechanism by utilizing the functionality provided by SGX.
Evolution of robust high speed optical-flow-based landing for autonomous MAVs

Automatic optimization of robotic behavior has been the long-standing goal of Evolutionary Robotics. Allowing the problem at hand to be solved by automation often leads to novel approaches and new insights. A common problem encountered with this approach is that when this optimization occurs in a simulated environment, the optimized policies are subject to the reality gap when implemented in the real world. This often results in sub-optimal behavior, if it works at all. This paper investigates the automatic optimization of neurocontrollers to perform quick but safe landing maneuvers for a quadrotor micro air vehicle using the divergence of the optical flow field of a downward looking camera. The optimized policies showed that a piece-wise linear control scheme is more effective than the simple linear scheme commonly used, something not yet considered by human designers. Additionally, we show the utility in using abstraction on the input and output of the controller as a tool to improve the robustness of the optimized policies to the reality gap by testing our policies optimized in simulation on real world vehicles. We tested the neurocontrollers using two different methods to generate and process the visual input, one using a conventional CMOS camera and one a dynamic vision sensor, both of which perform significantly differently than the simulated sensor. The use of the abstracted input resulted in near seamless transfer to the real world with the controllers showing high robustness to a clear reality gap.
Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers

Over the years, data have become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for clustering (unsupervised classification), as well as semi-supervised and supervised classification. Many methods have been proposed in the literature for two-way (multivariate) data and quite recently methods have been presented for three-way (matrix variate) data. One such method is the mixtures of matrix variate bilinear factor analyzers (MMVBFA) model. Herein, we propose a total of 64 parsimonious MMVBFA models. Simulated and real data are used for illustration.
An Improved Residual Chi-Square Test Fault Isolation Approach in Four-Gyro SINS

In this paper, an improved residual chi-square test fault isolation approach is proposed. In order to improve reliability of strapdown inertial navigation system (SINS), redundant SINS composed of redundant inertial sensors is applied. Among redundant SINS, four-gyro SINS has a wide range of applications as it supplies big reliability considering cost and volume. Fault isolation (FI) can isolate a fault when a fault occurs in redundant SINS. Generalized likelihood test (GLT) fault isolation based on kalman filter (KF) effectively isolate a fault because of its high sensitivity, small calculation and easy implementation. Nevertheless, GLT fault isolation based on KF cannot recognize a fault in four-gyro SINS. In this paper, first, a new observation model is introduced based on error models of a redundant SINS. In addition, a residual vector for isolation based on KF generated by gyros and star sensors is designed. Second, a separate residual chi-square test fault isolation approach for each gyro is proposed to recognize a fault. Third, an average separate residual vector and new isolation threshold are designed to reduce isolation time. At last, a star sensor is employed to obtain angular velocity, which provides angular velocity baseline information for the proposed isolation approach. Simulation shows that the proposed approach is useful to recognize a fault in four-gyro SINS.
Batch-shaping for learning conditional channel gated networks

We present a method that trains large capacity neural networks with significantly improved accuracy and lower dynamic computational cost. This is achieved by gating the deep-learning architecture on a fine-grained-level. Individual convolutional maps are turned on/off conditionally on features in the network. To achieve this, we introduce a new residual block architecture that gates convolutional channels in a fine-grained manner. We also introduce a generally applicable tool batch-shaping that matches the marginal aggregate posteriors of features in a neural network to a pre-specified prior distribution. We use this novel technique to force gates to be more conditional on the data. We present results on CIFAR-10 and ImageNet datasets for image classification, and Cityscapes for semantic segmentation. Our results show that our method can slim down large architectures conditionally, such that the average computational cost on the data is on par with a smaller architecture, but with higher accuracy. In particular, on ImageNet, our ResNet50 and ResNet34 gated networks obtain 74.60% and 72.55% top-1 accuracy compared to the 69.76% accuracy of the baseline ResNet18 model, for similar complexity. We also show that the resulting networks automatically learn to use more features for difficult examples and fewer features for simple examples.
Image-guided Neural Object Rendering

We present a novel method for photo-realistic re-rendering of reconstructed objects. The digital reproduction of object appearances is of paramount importance nowadays. Augmented and virtual reality relies on such 3D content. It enables virtual showrooms, virtual tours & sightseeing, the digital inspection of historical artifacts and many other applications. Classical approaches use methods to reconstruct the geometry of an object and textures to capture the appearance properties. Instead, we propose a learned image-guided rendering technique that combines the benefits of image-based rendering and GAN-based image synthesis. A core component of our work is the handling of view-dependent effects. Specifically, we directly train an object-specific deep neural network to synthesize the view-dependent appearance of an object. As input data we are using an RGB video of the object. This video is used to reconstruct a proxy geometry of the object via multi-view stereo. Based on this 3D proxy, the appearance of a captured view can be warped into a new target view. This warping assumes diffuse surfaces, in case of view-dependent effects, such as specular highlights, it leads to artifacts. To this end, we propose EffectsNet, a deep neural network that predicts view-dependent effects. Based on these estimations, we are able to convert observed images to diffuse images. These diffuse images can be projected into other views. In the target view, our pipeline reinserts the new view-dependent effects. To composite multiple reprojected images to a final output, we learn a composition network that outputs photo-realistic results. Using this image-guided approach, the network does not have to allocate capacity on ``''remembering'' object appearance, instead it learns how to combine the appearance of captured images. We demonstrate the effectiveness of our approach both qualitatively and quantitatively on synthetic as well as on real data.
Feature-Representation Transfer Learning for Human Activity Recognition

Many human-centered intelligent systems require information about the activities being performed by the user for the systems to function optimally. Human activity recognition (HAR) is at the core of such systems. Activity recognition requires vast amounts of labeled training data to perform adequately under a variety of circumstances. The lack of enough labeled training data led to transfer learning (TL), a phenomenon that uses knowledge learned from one task's dataset to easily perform a different task. In this paper, we show how TL can be used to improve the recognition of human activities with a small number of data samples. Using a convolutional neural network - long short-term memory (CNN - LSTM) deep learning ensemble classifier, we show how features learned from activities with motion enable us to easily learn features of stationery activities, even with a small dataset. TL improved model generalizability and reduced overfitting. To evaluate the performance, we used the UCI HAR dataset that contains 6 activities which was split into two sub tasks. The accuracy increased by over 4% whereas the loss decreased by 30% between a base model and the TL model. We also present the opportunities that using TL presents to the field of human activity recognition where new activities that have very small amounts of training data can be learned using data from already existing datasets.
Practical Encrypted Network Traffic Pattern Matching for Secure Middleboxes

Network Function Virtualisation (NFV) advances the adoption of composable software middleboxes. Accordingly, cloud data centres become major NFV vendors for enterprise traffic processing. Due to the privacy concern of traffic redirection to the cloud, secure middlebox systems (e.g., BlindBox) draw much attention; they can process encrypted packets against encrypted rules directly. However, most of the existing systems supporting pattern matching based network functions require the enterprise gateway to tokenise packet payloads via sliding windows. Such tokenisation induces a considerable communication overhead, which can be over 100$\times$ to the packet size. To overcome this bottleneck, in this paper, we propose the first bandwidth-efficient encrypted pattern matching protocol for secure middleboxes. We resort to a primitive called symmetric hidden vector encryption (SHVE), and propose a variant of it, aka SHVE+, to achieve constant and moderate communication cost. To speed up, we devise encrypted filters to reduce the number of accesses to SHVE+ during matching highly. We formalise the security of our proposed protocol and conduct comprehensive evaluations over real-world rulesets and traffic dumps. The results show that our design can inspect a packet over 20k rules within 100 $\mu$s. Compared to prior work, it brings a saving of $94\%$ in bandwidth consumption.