Reviewerly/exHarmony huggingface dataset:
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-linear Activations

In the recent quest for trustworthy neural networks, we present Spiking Neural Network (SNN) as a potential candidate for inherent robustness against adversarial attacks. In this work, we demonstrate that adversarial accuracy of SNNs under gradient-based attacks is higher than their non-spiking counterparts for CIFAR datasets on deep VGG and ResNet architectures, particularly in blackbox attack scenario. We attribute this robustness to two fundamental characteristics of SNNs and analyze their effects. First, we exhibit that input discretization introduced by the Poisson encoder improves adversarial robustness with reduced number of timesteps. Second, we quantify the amount of adversarial accuracy with increased leak rate in Leaky-Integrate-Fire (LIF) neurons. Our results suggest that SNNs trained with LIF neurons and smaller number of timesteps are more robust than the ones with IF (Integrate-Fire) neurons and larger number of timesteps. Also we overcome the bottleneck of creating gradient-based adversarial inputs in temporal domain by proposing a technique for crafting attacks from SNN (https://github.com/ssharmin/spikingNN-adversarial-attack).
Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty

In this paper, we carry out a review of the grey wolf, the firefly and the bat algorithms. We identify the concepts involved in these three metaphor-based algorithms and compare them to those proposed in the context of particle swarm optimization. We provide compelling evidence that the grey wolf, the firefly, and the bat algorithms are not novel, but a reiteration of ideas introduced first for particle swarm optimization and reintroduced years later using new natural metaphors. These three algorithms can therefore be added to the growing list of metaphor-based algorithms—to which already belong algorithms such as harmony search and intelligent water drops—that are nothing else than repetitions of old ideas hidden by the usage of new terminology.
FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Anomalies and faults can be detected, and their causes verified, using both data-driven and knowledge-driven techniques. Data-driven techniques can adapt their internal functioning based on the raw input data but fail to explain the manifestation of any detection. Knowledge-driven techniques inherently deliver the cause of the faults that were detected but require too much human effort to set up. In this paper, we introduce FLAGS, the Fused-AI interpretabLe Anomaly Generation System, and combine both techniques in one methodology to overcome their limitations and optimize them based on limited user feedback. Semantic knowledge is incorporated in a machine learning technique to enhance expressivity. At the same time, feedback about the faults and anomalies that occurred is provided as input to increase adaptiveness using semantic rule mining methods. This new methodology is evaluated on a predictive maintenance case for trains. We show that our method reduces their downtime and provides more insight into frequently occurring problems.
Label Propagation with Augmented Anchors: A Simple Semi-supervised Learning Baseline for Unsupervised Domain Adaptation

Motivated by the problem relatedness between unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), many state-of-the-art UDA methods adopt SSL principles (e.g., the cluster assumption) as their learning ingredients. However, they tend to overlook the very domain-shift nature of UDA. In this work, we take a step further to study the proper extensions of SSL techniques for UDA. Taking the algorithm of label propagation (LP) as an example, we analyze the challenges of adopting LP to UDA and theoretically analyze the conditions of affinity graph/matrix construction in order to achieve better propagation of true labels to unlabeled instances. Our analysis suggests a new algorithm of Label Propagation with Augmented Anchors (A $$^2$$ LP), which could potentially improve LP via generation of unlabeled virtual instances (i.e., the augmented anchors) with high-confidence label predictions. To make the proposed A $$^2$$ LP useful for UDA, we propose empirical schemes to generate such virtual instances. The proposed schemes also tackle the domain-shift challenge of UDA by alternating between pseudo labeling via A $$^2$$ LP and domain-invariant feature learning. Experiments show that such a simple SSL extension improves over representative UDA methods of domain-invariant feature learning, and could empower two state-of-the-art methods on benchmark UDA datasets. Our results show the value of further investigation on SSL techniques for UDA problems.
Deep image captioning using an ensemble of CNN and LSTM based deep neural networks

The paper is concerned with the problem of Image Caption Generation. The purpose of this paper is to create a deep learning model to generate captions for a given image by decoding the information available in the image. For this purpose, a custom ensemble model was used, which consisted of an Inception model and a 2-layer LSTM model, which were then concatenated and dense layers were added. The CNN part encodes the images and the LSTM part derives insights from the given captions. For comparative study, GRU and Bi-directional LSTM based models are also used for the caption generation to analyze and compare the results. For the training of images, the dataset used is the flickr8k dataset and for word embedding, dataset used is GloVe Embeddings to generate word vectors for each word in the sequence. After vectorization, Images are then fed into the trained model and inferred to create new auto-generated captions. Evaluation of the results was done using Bleu Scores. The Bleu-4 score obtained in the paper is 55.8%, and using LSTM, GRU, and Bi-directional LSTM respectively.
A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel

This paper presents a new parameters estimation method for the single diode model and the double diode model of photovoltaic cells. A new application of the slime mould algorithm (SMA) stochastic optimization method represents one of the main contributions in this paper. The employment of the slime mould algorithm provides highly accurate and fast determination procedures of photovoltaic cells parameters. The slime mould algorithm stochastic optimization provides unique mathematical modelling of the optimization problem through using adaptive weights. These adaptive weights emulate the process of generating positive and negative feedbacks in the propagated wave of the slime mould. Moreover, the slime mould algorithm is successful at accurately extracting the global optimal values for the solar photovoltaic cell parameters. It has the ability to handle the non-linearity and multi-modal properties of the photovoltaic cell characteristics. The proposed method provides a generalized solution for precisely determining the parameters for various photovoltaic cells technologies. Comprehensive comparisons of the proposed slime mould algorithm with existing photovoltaic cell parameter extraction methods are performed in the paper. The results confirm the superior performance and accuracy of the proposed slime mould algorithm over the existing methods.
Gradient-Induced Co-Saliency Detection

Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images. In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. We first abstract a consensus representation for a group of images in the embedding space; then, by comparing the single image with consensus representation, we utilize the feedback gradient information to induce more attention to the discriminative co-salient features. In addition, due to the lack of Co-SOD training data, we design a jigsaw training strategy, with which Co-SOD networks can be trained on general saliency datasets without extra pixel-level annotations. To evaluate the performance of Co-SOD methods on discovering the co-salient object among multiple foregrounds, we construct a challenging CoCA dataset, where each image contains at least one extraneous foreground along with the co-salient object. Experiments demonstrate that our GICD achieves state-of-the-art performance. Our codes and dataset are available at https://mmcheng.net/gicd/.
Randomized Extended Average Block Kaczmarz for Solving Least Squares

Randomized iterative algorithms have recently been proposed to solve large-scale linear systems. In this paper, we present a simple randomized extended average block Kaczmarz algorithm that exponentially converges in the mean square to the unique minimum norm least squares solution of a given linear system of equations. The proposed algorithm is pseudoinverse-free and therefore different from the projection-based randomized double block Kaczmarz algorithm of Needell, Zhao, and Zouzias [Linear Algebra Appl., 484 (2015), pp. 322--343]. We emphasize that our method works for all types of linear systems (consistent or inconsistent, overdetermined or underdetermined, full-rank or rank-deficient). Moreover, our approach can be implemented for parallel computation, yielding remarkable improvements in computational time. Numerical examples are given to show the efficiency of the new algorithm.
Graph-Temporal LSTM Networks for Skeleton-Based Action Recognition

Human action recognition is one of the challenging and active research fields. Recently, spatio-temporal graph convolutions for skeleton-based action recognition have attracted much attention. Several strategies, such as temporal downsampling, convolution striding, and temporal pooling, are used to handle long action sequences. Recurrent neural networks are typically used for the processing of sequential data. In this paper, we propose a deep architecture that combines spatio-temporal graph convolution and graph-temporal long short-term memory (GT-LSTM) for skeleton-based human action recognition. Initially, topology-learnable spatio-temporal graph convolutions are applied to learn the local spatio-temporal features of graph nodes and adaptively evolve graph topologies. Then, GT-LSTM successively performs the spatio-temporal feature fusion with the node sequence and the temporal dimension, for the final recognition. Experimental results on the NTU RGB+D and Kinetics-Skeleton datasets demonstrate that the proposed architecture can effectively perform graph node information aggregation, graph topology evolution, and spatio-temporal graph feature fusion. liu2017skeleton.
Generative Low-Bitwidth Data Free Quantization

Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedded devices. Existing quantization methods require original data for calibration or fine-tuning to get better performance. However, in many real-world scenarios, the data may not be available due to confidential or private issues, thereby making existing quantization methods not applicable. Moreover, due to the absence of original data, the recently developed generative adversarial networks (GANs) cannot be applied to generate data. Although the full-precision model may contain rich data information, such information alone is hard to exploit for recovering the original data or generating new meaningful data. In this paper, we investigate a simple-yet-effective method called Generative Low-bitwidth Data Free Quantization (GDFQ) to remove the data dependence burden. Specifically, we propose a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model. With the help of generated data, we can quantize a model by learning knowledge from the pre-trained model. Extensive experiments on three data sets demonstrate the effectiveness of our method. More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method. Code is available at https://github.com/xushoukai/GDFQ.
GSLICE

The increasing demand for cloud-based inference services requires the use of Graphics Processing Unit (GPU). It is highly desirable to utilize GPU efficiently by multiplexing different inference tasks on the GPU. Batched processing, CUDA streams and Multi-process-service (MPS) help. However, we find that these are not adequate for achieving scalability by efficiently utilizing GPUs, and do not guarantee predictable performance.
Ligero++: A New Optimized Sublinear IOP

This paper follows the line of works that design concretely efficient transparent sublinear zero-knowledge Interactive Oracle Proofs (IOP). Arguments obtained via this paradigm have the advantages of not relying on public-key cryptography, not requiring a trusted setup, and resistance to known quantum attacks. In the realm of transparent systems, Ligero and Aurora stand out with incomparable advantages where the former has a fast prover algorithm somewhat succinct proofs and the latter has somewhat fast prover and succinct proofs. In this work, we introduce Ligero++ that combines the best features of both approaches to achieve the best of both worlds. We implement our protocol and benchmark the results.
PyZX: Large Scale Automated Diagrammatic Reasoning

The ZX-calculus is a graphical language for reasoning about ZX-diagrams, a type of tensor networks that can represent arbitrary linear maps between qubits. Using the ZX-calculus, we can intuitively reason about quantum theory, and optimise and validate quantum circuits. In this paper we introduce PyZX, an open source library for automated reasoning with large ZX-diagrams. We give a brief introduction to the ZX-calculus, then show how PyZX implements methods for circuit optimisation, equality validation, and visualisation and how it can be used in tandem with other software. We end with a set of challenges that when solved would enhance the utility of automated diagrammatic reasoning.
Survival exploration strategies for Harris Hawks Optimizer

This paper proposes new versions of Harris Hawks Optimizer (HHO) incorporated the survival-of-the-fittest principle of evolutionary algorithms. HHO is the recent swarm-based optimization algorithm imitating the surprise pounce behaviour of Harris’ hawks chasing style. HHO can show different patterns of the exploration and exploitation. It has a simple and time-varying structure, which further assist a smooth transition between the core phases. It has two main phases to iterate toward the optimal solution: exploration and exploitation. In the exploration phase, the current solution is either randomly modified based on any solution selected randomly or rebuilt from scratch. In evolutionary algorithms, selecting any solution from swarm basically relies on the natural selection principle of the survival-of-the-fittest to accelerate convergence. To make use of such principle, three selection strategies (i.e., tournament, proportional and linear rank-based methods) are employed in the exploration phase of HHO and introduces three new versions, which are Tournament HHO (THHO), Proportional HHO (PHHO), and Linear-Rank HHO (LHHO). In order to evaluate the performance of the proposed HHO versions, 23 well-regarded benchmark functions with various sizes and complexities are utilized as well as three real-world engineering problems. The sensitivity of proposed HHO versions to their parameter settings are studied and analyzed. Thereafter, a scalability study is conducted to show the effect of the population dimensions on the proposed HHO versions. Comparative evaluation shows that THHO version has superiority over other proposed HHO versions. Furthermore, the proposed HHO versions show enhanced trade off between the exploratory and exploitative trends and a better local optima avoidance. They are able to produce viable results competitively comparable with other eleven state-of-the-art methods using the same benchmark functions. Interestingly, the proposed variants of HHO are able to yield new results for some benchmark functions. Furthermore, three real-world engineering optimization problem of IEEE CEC2011 are also used in the evaluation process. Again, the proposed variants of HHO are able to achieve the best results. The information, guides and supplementary accessible files for this research will be publicly available at https://aliasgharheidari.com.
A volumetric framework for quantum computer benchmarks

We propose a very large family of benchmarks for probing the performance of quantum computers. We call them volumetric benchmarks (VBs) because they generalize IBM's benchmark for measuring quantum volume \cite{Cross18}. The quantum volume benchmark defines a family of square circuits whose depth $d$ and width $w$ are the same. A volumetric benchmark defines a family of rectangular quantum circuits, for which $d$ and $w$ are uncoupled to allow the study of time/space performance trade-offs. Each VB defines a mapping from circuit shapes -- $(w,d)$ pairs -- to test suites $\mathcal{C}(w,d)$. A test suite is an ensemble of test circuits that share a common structure. The test suite $\mathcal{C}$ for a given circuit shape may be a single circuit $C$, a specific list of circuits $\{C_1\ldots C_N\}$ that must all be run, or a large set of possible circuits equipped with a distribution $Pr(C)$. The circuits in a given VB share a structure, which is limited only by designers' creativity. We list some known benchmarks, and other circuit families, that fit into the VB framework: several families of random circuits, periodic circuits, and algorithm-inspired circuits. The last ingredient defining a benchmark is a success criterion that defines when a processor is judged to have "passed" a given test circuit. We discuss several options. Benchmark data can be analyzed in many ways to extract many properties, but we propose a simple, universal graphical summary of results that illustrates the Pareto frontier of the $d$ vs $w$ trade-off for the processor being benchmarked. [1] A. Cross, et al., Phys. Rev. A, 100, 032328, September 2019.
Foundational Oracle Patterns: Connecting Blockchain to the Off-Chain World

Blockchain has evolved into a platform for decentralized applications, with beneficial properties like high integrity, transparency, and resilience against censorship and tampering. However, blockchains are closed-world systems which do not have access to external state. To overcome this limitation, oracles have been introduced in various forms and for different purposes. However so far common oracle best practices have not been dissected, classified, and studied in their fundamental aspects. In this paper, we address this gap by studying foundational blockchain oracle patterns in two foundational dimensions characterising the oracles: (i) the data flow direction, i.e., inbound and outbound data flow, from the viewpoint of the blockchain; and (ii) the initiator of the data flow, i.e., whether it is push or pull-based communication. We provide a structured description of the four patterns in detail, and discuss an implementation of these patterns based on use cases. On this basis we conduct a quantitative analysis, which results in the insight that the four different patterns are characterized by distinct performance and costs profiles.
State-Aware Anti-Drift Object Tracking

Correlation filter (CF) based trackers have aroused increasing attentions in visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter is trained in order to discriminate the target from its surrounding background. Considering that the target always undergoes external and internal interference during tracking procedure, the trained filter should take consideration of not only the external distractions but also the target appearance variation synchronously. To this end, we present a State-aware Anti-drift Tracker (SAT) in this paper, which jointly model the discrimination and reliability information in filter learning. Specifically, global context patches are incorporated into filter training stage to better distinguish the target from backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the filter to focus on more reliable regions suitable for tracking. We show that the proposed optimization problem could be efficiently solved using Alternative Direction Method of Multipliers and fully carried out in Fourier domain. Extensive experiments are conducted on OTB-100 datasets to compare the SAT tracker (both hand-crafted feature and CNN feature) with other relevant state-of-the-art methods. Both quantitative and qualitative evaluations further demonstrate the effectiveness and robustness of the proposed work.
Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems

We present a quantum eigenstate filtering algorithm based on quantum signal processing (QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target eigenstate of a given Hamiltonian, if we have access to an initial state with non-trivial overlap with the target eigenstate and have a reasonable lower bound for the spectral gap. We apply this algorithm to the quantum linear system problem (QLSP), and present two algorithms based on quantum adiabatic computing (AQC) and quantum Zeno effect respectively. Both algorithms prepare the final solution as a pure state, and achieves the near optimal<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mover><mml:mi class="MJX-tex-caligraphic" mathvariant="script">O</mml:mi><mml:mo class="MJX-tex-caligraphic" mathvariant="script">~</mml:mo></mml:mover></mml:mrow></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>d</mml:mi><mml:mi>κ</mml:mi><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>ϵ</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math>query complexity for a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>-sparse matrix, where<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>κ</mml:mi></mml:math>is the condition number, and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math>is the desired precision. Neither algorithm uses phase estimation or amplitude amplification.