Reviewerly/exHarmony huggingface dataset:
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

In this paper, we propose a novel and practical mechanism to enable the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the profile of a DNN model's decision boundary can be uniquely characterized by its Universal Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and piracy models' subspaces are more consistent with victim model's subspace compared with non-piracy model. Based on this, we propose a UAP fingerprinting method for DNN models and train an encoder via contrastive learning that takes fingerprints as inputs, outputs a similarity score. Extensive studies show that our framework can detect model Intellectual Property (IP) breaches with confidence > 99.99 % within only 20 fingerprints of the suspect model. It also has good generalizability across different model architectures and is robust against post-modifications on stolen models.
FPGA Implementation of a Chaotic Map with No Fixed Point

The employment of chaotic maps in a variety of applications such as cryptosecurity, image encryption schemes, communication schemes, and secure communication has been made possible thanks to their properties of high levels of complexity, ergodicity, and high sensitivity to the initial conditions, mainly. Of considerable interest is the implementation of these dynamical systems in electronic devices such as field programmable gate arrays (FPGAs) with the intention of experimentally reproducing their dynamics, leading to exploiting their chaotic properties in real phenomena. In this work, the implementation of a one-dimensional chaotic map that has no fixed points is performed on an FPGA device with the objective of being able to reproduce its chaotic behavior as well as possible. The chaotic behavior of the introduced system is determined by estimating the Lyapunov exponents and its chaotic behavior is also analyzed using bifurcation diagrams. Simulations of the system are realized via Matlab, as well as in C and the very high-speed integrated circuit (VHSIC) hardware description language (VHDL). Experimental results on FPGA show that they are like those obtained in the simulations; therefore, this chaotic dynamical system could be used as an element in some encryption schemes such as in the generation of cryptographically secure pseudorandom numbers.
Probabilistic solar forecasting: Benchmarks, post-processing, verification

Probabilistic solar forecasts may take the form of predictive probability distributions, ensembles, quantiles, or interval forecasts. State-of-the-art approaches build on input from numerical weather prediction (NWP) models and post-processing with statistical and machine learning methods. We propose a probabilistic benchmark based on a deterministic forecast of clear-sky irradiance, introduce new methods for post-processing that merge statistical techniques with modern neural networks, discuss methods for spatio-temporal scenario forecasts, and illustrate the assessment of predictive ability via proper scoring rules and calibration checks. We expect future solar forecasting efforts to be increasingly probabilistic, and encourage continuing close interaction with operational weather prediction, where innovations based on sophisticated neural networks supplement and challenge traditional approaches.
Shadow process tomography of quantum channels

Quantum process tomography is a critical capability for building quantum computers, enabling quantum networks, and understanding quantum sensors. Like quantum state tomography, the process tomography of an arbitrary quantum channel requires a number of measurements that scales exponentially in the number of quantum bits affected. However, the recent field of shadow tomography, applied to quantum states, has demonstrated the ability to extract key information about a state with only polynomially many measurements. In this work, we apply the concepts of shadow state tomography to the challenge of characterizing quantum processes. We make use of the Choi isomorphism to directly apply rigorous bounds from shadow state tomography to shadow process tomography, and we find additional bounds on the number of measurements that are unique to process tomography. Our results, which include algorithms for implementing shadow process tomography, enable new techniques including evaluation of channel concatenation and the application of channels to shadows of quantum states. This provides a dramatic improvement for understanding large-scale quantum systems.
Model-Based Clustering

This chapter introduces the model-based clustering is related to standard heuristic clustering methods and an overview of different ways to specify the cluster model. It provides the clustering problem, strategies for selecting a suitable mixture model together with the appropriate clustering base. The chapter discusses how to obtain an identified model, derive a partition of the data, characterize the cluster distributions, gain insights through suitable visualizations and validate the clustering. It explores the model-based clustering has been used in a range of different applications from which also methodological advances have emerged. Mixture models extend the toolbox of clustering methods available to the data analyst. Heuristic clustering methods are based on the definition of similarities or dissimilarities between observations and groups of observations. In hierarchical clustering some linkage methods, that is, distance definitions between groups of observations, also lead to solutions with high compactness, such as complete linkage.
Sparse Winograd Convolutional Neural Networks on Small-scale Systolic Arrays

The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators. However, state-of-art implementations seldom consider the balance between high throughput of computation power and the ability of the memory subsystem to support it. In this paper, we implement a framework on FPGA by combining the sparse Winograd convolution, clusters of small-scale systolic arrays, and a tailored recursive Z-Morton memory layout design. We also provide an analytical model analysis for the general Winograd convolution algorithm as a design reference. Experimental results on various CNN models show that it achieves very high computation resource utilization, 20x~30x energy efficiency, and more than 5x speedup compared with the dense implementation.
Massively parallel approximation algorithms for edit distance and longest common subsequence

String similarity measures are among the most fundamental problems in computer science. The notable examples are edit distance (ED) and longest common subsequence (LCS). These problems find their applications in various contexts such as computational biology, text processing, compiler optimization, data analysis, image analysis, etc. In this work, we revisit edit distance and longest common subsequence in the parallel settings. We present massively parallel algorithms for both problems that are optimal in the following senses:• The approximation factor of our algorithms is 1 + ϵ.• The round complexity of our algorithms is constant.• The total running time of our algorithms over all machines is O(n2). This matches the running time of the best-known solutions for approximating edit distance and longest common subsequence within a 1+ϵ factor in the sequential setting.Our result for edit distance substantially improves the massively parallel algorithm of [15] in terms of approximation factor, round complexity, number of machines, and total running time. Our unified approach to tackle both problems is to divide one of the strings into smaller blocks and try to locally predict which intervals of the other string correspond to each block in an optimal solution.Our main technical contribution is a novel parallel algorithm for computing a set of compositions, and recursively decomposing each function into a set of smaller iterative compositions (in terms of memory needed to solve the problem). These two methods together give us a strong tool for approximating combinatorial problems. For instance, LCS can be formulated as a recursive composition of functions and therefore this tool enables us to approximate LCS within a factor 1 + ϵ. Indeed, we recursively decompose the problem until we are able to compute the solution on a single machine. Since our methods are quite general, we expect this technique to find its applications in other combinatorial problems as well.
An Expert Derived Feedforward Histology Module Improves Pattern Recognition Efficiency in Novice Students

Histology is a visually oriented, foundational anatomical sciences subject in professional health curricula that has seen a dramatic reduction in educational contact hours and an increase in content migration to a digital platform. While the digital migration of histology laboratories has transformed histology education, few studies have shown the impact of this change on visual literacy development, a critical competency in histology. The objective of this study was to assess whether providing a video clip of an expert's gaze while completing leukocyte identification tasks would increase the efficiency and performance of novices completing similar identification tasks. In a randomized study, one group of novices (n = 9) was provided with training materials that included expert eye gaze, while the other group (n = 12) was provided training materials with identical content, but without the expert eye gaze. Eye movement parameters including fixation rate and total scan path distance, and performance measures including time-to-task-completion and accuracy, were collected during an identification task assessment. Compared to the control group, the average fixation duration was 13.2% higher (P < 0.02) and scan path distance was 35.0% shorter in the experimental group (P = 0.14). Analysis of task performance measures revealed no significant difference between the groups. These preliminary results suggest a more efficient search performed by the experimental group, indicating the potential efficacy of training using an expert's gaze to enhance visual literacy development. With further investigation, such feedforward enhanced training methods could be utilized for histology and other visually oriented subjects.
Improvement in Classification Performance Based on Target Vector Modification for All-Transfer Deep Learning

This paper proposes a target vector modification method for the all-transfer deep learning (ATDL) method. Deep neural networks (DNNs) have been used widely in many applications; however, the DNN has been known to be problematic when large amounts of training data are not available. Transfer learning can provide a solution to this problem. Previous methods regularize all layers, including the output layer, by estimating the relation vectors, which are then used instead of one-hot target vectors of the target domain. These vectors are estimated by averaging the target domain data of each target domain label in the output space. This method improves the classification performance, but it does not consider the relation between the relation vectors. From this point of view, we propose a relation vector modification based on constrained pairwise repulsive forces. High pairwise repulsive forces provide large distances between the relation vectors. In addition, the risk of divergence is mitigated by the constraint based on distributions of the output vectors of the target domain data. We apply our method to two simulation experiments and a disease classification using two-dimensional electrophoresis images. The experimental results show that reusing all layers through our estimation method is effective, especially for a significantly small number of the target domain data.
Neural machine translation system for the Kazakh language based on synthetic corpora

The lack of big parallel data is present for the Kazakh language. This problem seriously impairs the quality of machine translation from and into Kazakh. This article considers the neural machine translation of the Kazakh language on the basis of synthetic corpora. The Kazakh language belongs to the Turkic languages, which are characterised by rich morphology. Neural machine translation of natural languages requires large training data. The article will show the model for the creation of synthetic corpora, namely the generation of sentences based on complete suffixes for the Kazakh language. The novelty of this approach of the synthetic corpora generation for the Kazakh language is the generation of sentences on the basis of the complete system of suffixes of the Kazakh language. By using generated synthetic corpora we are improving the translation quality in neural machine translation of Kazakh-English and Kazakh-Russian pairs.
Manipulating photonic spin accumulation with a magnetic field

In this paper, we propose a simple and effective method to manipulate photonic spin accumulation with an applied magnetic field. When a linearly polarized Gaussian light beam is reflected from the prism-air interface at the Brewster angle, the magnitude and direction of the photonic spin accumulation can be flexibly modulated by adjusting the applied magnetic field. Importantly, the maximum displacement of photonic spin accumulation can reach about 39.14μm, which provides the opportunity to directly observe photonic spin accumulation without weak measurements. These findings may hold potential applications for manipulating and detecting the electron spin current, leading to the development of a new spintronic device.
Multi-target regression via target specific features

Multi-target regression (MTR) has recently attracted great interest by the research community due to its capability of learning multiple related regression tasks. Existing approaches to MTR learn prediction models based on an identical set of features, which may be suboptimal since different targets may possess specific characteristics of their own. In this paper, we propose a method MTR-TSF that deals with the MTR tasks by learning target specific features (TSF). Firstly, a hierarchical clustering algorithm is applied to the output space of training data to reveal the similarities among multiple targets. Then, a classification and regression tree boosting method (CART-boosting) is used to generate a dependent similarity matrix for each target. Finally, target specific features are learned by querying the corresponding dependent similarity matrix and conducting clustering analysis on the training data. MTR-TSF method leverages the pertinent and discriminative features of each target and the dependence among multiple target to improve the overall performance of MTR. Experimental results on 18 real-world datasets demonstrate that MTR-TSF can achieve competitive performance against representative state-of-the-art MTR methods.
Representations and Optimizations for Embedded Parallel Dataflow Languages

Parallel dataflow engines such as Apache Hadoop, Apache Spark, and Apache Flink are an established alternative to relational databases for modern data analysis applications. A characteristic of these systems is a scalable programming model based on distributed collections and parallel transformations expressed by means of second-order functions such as map and reduce. Notable examples are Flink’s DataSet and Spark’s RDD programming abstractions. These programming models are realized as EDSLs—domain specific languages embedded in a general-purpose host language such as Java, Scala, or Python. This approach has several advantages over traditional external DSLs such as SQL or XQuery. First, syntactic constructs from the host language (e.g., anonymous functions syntax, value definitions, and fluent syntax via method chaining) can be reused in the EDSL. This eases the learning curve for developers already familiar with the host language. Second, it allows for seamless integration of library methods written in the host language via the function parameters passed to the parallel dataflow operators. This reduces the effort for developing analytics dataflows that go beyond pure SQL and require domain-specific logic. At the same time, however, state-of-the-art parallel dataflow EDSLs exhibit a number of shortcomings. First, one of the main advantages of an external DSL such as SQL—the high-level, declarative Select-From-Where syntax—is either lost completely or mimicked in a non-standard way. Second, execution aspects such as caching, join order, and partial aggregation have to be decided by the programmer. Optimizing them automatically is very difficult due to the limited program context available in the intermediate representation of the DSL. In this article, we argue that the limitations listed above are a side effect of the adopted type-based embedding approach. As a solution, we propose an alternative EDSL design based on quotations. We present a DSL embedded in Scala and discuss its compiler pipeline, intermediate representation, and some of the enabled optimizations. We promote the algebraic type of bags in union representation as a model for distributed collections and its associated structural recursion scheme and monad as a model for parallel collection processing. At the source code level, Scala’s comprehension syntax over a bag monad can be used to encode Select-From-Where expressions in a standard way. At the intermediate representation level, maintaining comprehensions as a first-class citizen can be used to simplify the design and implementation of holistic dataflow optimizations that accommodate for nesting and control-flow. The proposed DSL design therefore reconciles the benefits of embedded parallel dataflow DSLs with the declarativity and optimization potential of external DSLs like SQL.
The Complexity of Making the Gradient Small in Stochastic Convex Optimization

We give nearly matching upper and lower bounds on the oracle complexity of finding $\epsilon$-stationary points ($\| \nabla F(x) \| \leq\epsilon$) in stochastic convex optimization. We jointly analyze the oracle complexity in both the local stochastic oracle model and the global oracle (or, statistical learning) model. This allows us to decompose the complexity of finding near-stationary points into optimization complexity and sample complexity, and reveals some surprising differences between the complexity of stochastic optimization versus learning. Notably, we show that in the global oracle/statistical learning model, only logarithmic dependence on smoothness is required to find a near-stationary point, whereas polynomial dependence on smoothness is necessary in the local stochastic oracle model. In other words, the separation in complexity between the two models can be exponential, and that the folklore understanding that smoothness is required to find stationary points is only weakly true for statistical learning. Our upper bounds are based on extensions of a recent recursive regularization technique proposed by Allen-Zhu (2018). We show how to extend the technique to achieve near-optimal rates, and in particular show how to leverage the extra information available in the global oracle model. Our algorithm for the global model can be implemented efficiently through finite sum methods, and suggests an interesting new computational-statistical tradeoff.
Identifying behavioural changes for health monitoring applications using the advanced metering infrastructure

The rising demand for health and social care, and around the clock monitoring services, is increasing and are unsustainable under current care provisions. Consequently, a safe and independent living environment is hard to achieve; yet the detection of sudden or worsening changes in a patient’s condition is vital for early intervention. The use of smart technologies in primary care delivery is increasing significantly. However, substantial research gaps remain in non-invasive and cost effective monitoring technologies. The inability to learn the unique characteristics of patients and their conditions seriously limits the effectiveness of most current solutions. The smart metering infrastructure provides new possibilities for a variety of applications that are unachievable using the traditional energy grid. By 2020, UK energy suppliers will install 50 million smart meters, therefore, providing access to a highly accurate sensing network. Each smart meter records the electrical load for a given property at 30 minute intervals. This granular data captures detailed habits and routines through the occupant’s interactions with electrical devices, enabling the detection and identification of alterations in behaviour. The research presented in this paper explores how this data could be used to achieve a safe living environment for people living with progressive neurodegenerative disorders.
CNN-based method for blotches and scratches detection in archived videos

Abstract In this work, we present a fully connected convolutional encoder-decoder for defects detection in archived video. The proposed method handles the detection of two of the most common archived video-related defects, namely blotches and scratches. It consists of two stages: (1) pixel-level classification and description of each video frame into defects pixels or not, by means of a novel CNN-based encoder-decoder architecture, and (2) spatio-temporal analysis to group and fine-tune the detections. For blotch detection, the learned features, extracted from an intermediate stage of the network, are used to evaluate the dissimilarity between the pre-selected regions in consecutive frames. For scratches detection, the morphology of scratches is used to eliminate false alarms. The experiments are performed on various video sequences suffering synthetic and real scratches and blotches. The results demonstrate the effectiveness of our approach and significant improvement against the most recent detectors.
Question Answering for Spoken Lecture Processing

This paper presents a question answering (QA) system developed for spoken lecture processing. The questions are presented to the system in written form and the answers are returned from lecture videos. In contrast to the widely studied reading comprehension style QA - the machine understands a passage of text and answers the questions related to that passage - our task introduces the challenge of searching the answers on longer text where the text corresponds to the erroneous transcripts of the lecture videos. Our initial experiments show that searching answers on longer text degrades the performance of the QA system drastically. Therefore, we propose splitting the transcriptions of lecture videos into short passages and determining passage-question matching using question aware passage representations. The proposed approach lets us utilize competitive neural network-based reading comprehension models for our task and improves the performance of the developed QA system.
Sub-event detection from twitter streams as a sequence labeling problem

Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.