Reviewerly/exHarmony huggingface dataset:
Iterative Back Modification for Faster Image Captioning

Current state-of-the-art image captioning systems generally produce a sentence from left to right, and every step is conditioned on the given image and previously generated words. Nevertheless, such autoregressive nature makes the inference process difficult to parallelize and leads to high captioning latency. In this paper, we propose a non-autoregressive approach for faster image caption generation. Technically, low-dimension continuous latent variables are shaped to capture semantic information and word dependencies from extracted image features before sentence decoding. Moreover, we develop an iterative back modification inference algorithm, which continuously refines the latent variables with a look back mechanism and parallelly generates the whole sentence based on the updated latent variables in a constant number of steps. Extensive experiments demonstrate that our method achieves competitive performance compared to prevalent autoregressive captioning models while significantly reducing the decoding time on average.
Laser Beams-Based Localization Methods for Boom-Type Roadheader Using Underground Camera Non-Uniform Blur Model

The efficiency of automatic underground tunneling is significantly depends on the localization accuracy and reliable for the Boom-type roadheader. In comparison with other underground equipment positioning methods, vision-based measurement has gained attention for its advantages of non-contact and no accumulated error. However, the harsh underground environment, especially the geometric errors brought by the vibration of the machine body to the underground camera model, has a certain influence on the accuracy and stability for the vision-based underground localization. In this paper, a laser beams-based localization methods for the machine body of Boom-type roadheader is presented, which can tackle the dense-dust, low illumination environment with the stray lights interference. Taking mining vibration into consideration, an underground camera non-uniform blur model that incorporate the two-layer glasses refraction effect was established to eliminate vibration errors. The blur model explicitly reveals the change of imaging optical path under the influence of vibration and double layer explosion-proof glass. On the basis of this, the underground laser beams extraction and positioning are presents, which is with well environmental adaptability, and the improved 2P3L (two-points-three-lines) localization model from line correspondences are developed. Experimental evaluation are designed to verify the performance of the proposed method, and the deblurring algorithm is investigated and evaluated. The results show that the proposed methods is effective to restore the blurred laser beams image that caused by the vibration, and can meet the precision need of roadheader body localization for roadway construction in coal mine.
Adversarial Examples Attack and Countermeasure for Speech Recognition System: A Survey

Speech recognition technology is affecting and changing the current human-computer interaction profoundly. Due to the remarkable progress of deep learning, the performance of the Automatic Speech Recognition (ASR) system has also increased significantly. As the core component of the speech assistant in the smartphone or other smart devices, ASR receives speech and responds accordingly, which allows us to control and interact with those devices remotely. However, speech adversarial samples where crafted by adding tiny perturbation to original speech, which can make the ASR system to generate malicious instructions while imperceptual to humans. This new attack brings several potential severe security risks to the deep-learning-based ASR system. In this paper, we provide a systematic survey on the speech adversarial examples. We first proposed a taxonomy of existing adversarial examples. Next, we give a brief introduction of existing adversarial examples for the acoustic system, especially for the ASR system, and summarize several major methods of generating the speech adversarial examples. Finally, after elaborating on the existing countermeasures of adversarial examples, we discuss the current challenges and countermeasures against speech adversarial examples. We also give several promising research directions on both making the attack constructing more realistic and the acoustic system more robust, respectively.
Weakest Preconditions in Fibrations

Weakest precondition transformers are useful tools in program verification. One of their key properties is compositionality, that is, the weakest precondition predicate transformer (wppt for short) associated to program f;g should be equal to the composition of the wppts associated to f and g. In this paper, we study the categorical structure behind wppts from a fibrational point of view. We characterize the wppts that satisfy compositionality as the ones constructed from the Cartesian lifting of a monad. We moreover show that Cartesian liftings of monads along lax slice categories bijectively correspond to Eilenberg-Moore monotone algebras. We then instantiate our techniques by deriving wppts for commonplace effects such as the maybe monad, the non-empty powerset monad, the counter monad or the distribution monad. We also show how to combine them to derive the wppts appearing in the literature of verification of probabilistic programs.
Intra-Frame Coding Using a Conditional Autoencoder

Exploiting spatial redundancy in images is responsible for a large gain in the performance of image and video compression. The main tool to achieve this is called intra-frame prediction. In most state-of-the-art video coders, intra prediction is applied in a block-wise fashion. Up to now angular prediction was dominant, providing a low-complexity method covering a large variety of content. With deep learning, however, it is possible to create prediction methods covering a wider range of content, being able to predict structures which traditional modes can not predict accurately. Using the conditional autoencoder structure, we are able to train a single artificial neural network which is able to perform multi-mode prediction. In this paper, we derive the approach from the general formulation of the intra-prediction problem and introduce two extensions for spatial mode prediction and for chroma prediction support. Moreover, we propose a novel latent-space-based cross component prediction. We show the power of our prediction scheme with visual examples and report average gains of 1.13% in Bjøntegaard delta rate in the luma component and 1.21% in the chroma component compared to VTM using only traditional modes.
When Is Deep Learning the Best Approach to Knowledge Tracing

Intelligent tutoring systems (ITSs) teach skills using learning-by-doing principles and provide learners with individualized feedback and materials adapted to their level of understanding. Given a learner's history of past interactions with an ITS, a learner performance model estimates the current state of a learner's knowledge and predicts her future performance. The advent of increasingly large scale datasets has turned deep learning models for learner performance prediction into competitive alternatives to classical Markov process and logistic regression models. In an extensive empirical comparison on nine real-world datasets, we ask which approach makes the most accurate predictions and in what conditions. Logistic regression - with the right set of features - leads on datasets of moderate size or containing or containing a very large number of interactions per student, whereas Deep Knowledge Tracing leads on datasets of large size or where precise temporal information matters most. Markov process methods, like Bayesian Knowledge Tracing, lag behind other approaches. We follow this analysis with ablation studies to determine what components of leading algorithms explain their performance and a discussion of model calibration (reliability), which is crucial for downstream applications of learner performance prediction models.
Improving End-to-End Speech-to-Intent Classification with Reptile

End-to-end spoken language understanding (SLU) systems have many advantages over conventional pipeline systems, but collecting in-domain speech data to train an end-to-end system is costly and time consuming. One question arises from this: how to train an end-to-end SLU with limited amounts of data? Many researchers have explored approaches that make use of other related data resources, typically by pre-training parts of the model on high-resource speech recognition. In this paper, we suggest improving the generalization performance of SLU models with a non-standard learning algorithm, Reptile. Though Reptile was originally proposed for model-agnostic meta learning, we argue that it can also be used to directly learn a target task and result in better generalization than conventional gradient descent. In this work, we employ Reptile to the task of end-to-end spoken intent classification. Experiments on four datasets of different languages and domains show improvement of intent prediction accuracy, both when Reptile is used alone and used in addition to pre-training.
TTS Skins: Speaker Conversion via ASR

We present a fully convolutional wav-to-wav network for converting between speakers' voices, without relying on text. Our network is based on an encoder-decoder architecture, where the encoder is pre-trained for the task of Automatic Speech Recognition, and a multi-speaker waveform decoder is trained to reconstruct the original signal in an autoregressive manner. We train the network on narrated audiobooks, and demonstrate multi-voice TTS in those voices, by converting the voice of a TTS robot.
Mask-guided noise restriction adversarial attacks for image classification

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are generated by adding small noises to the benign examples, but make a deep model output inaccurate predictions. The noises are often imperceptible to humans, but are more likely to be perceived for the images with plain backgrounds or increased noise size. To address this issue, we propose a mask-guided adversarial attack method to remove the noises of semantically irrelevant regions in the backgrounds and make the adversarial noises more imperceptible. In addition, we enhance the transferability of the adversarial examples by rotation input strategy. We first convert the image saliency maps produced by the salient object detection technique to binary masks, then we combine the proposed rotation input strategy with iterative attack method to generate stronger adversarial images, and use the binary masks to restrict the noises to the salient objects/regions at each iteration. Experimental results show that the noises of the resultant adversarial examples are far less visible than the vanilla global noise adversarial examples, and our best attack reaches an average success rate of 85.9% under the black-box attack setting, demonstrating the effectiveness of the proposed method.
Theoretical analysis of a nearly optimal analog quantum search

We analyze the possibility of modifying the original Farhi–Gutmann Hamiltonian algorithm in order to speed up the procedure for producing a suitably distributed unknown normalized quantum mechanical state. Such a modification is feasible provided only a nearly optimal fidelity is sought. We propose to select the lower bounds of the nearly optimal fidelity values such that their deviations from unit fidelity are less than the minimum error probability characterizing the optimum ambiguous discrimination scheme between the two non-orthogonal quantum states yielding the chosen nearly optimal fidelity values. Departing from the working assumptions of perfect state overlap and uniform distribution of the target state on the unit sphere in N-dimensional complex Hilbert space, we determine that the modified algorithm can indeed outperform the original analog counterpart of a quantum search algorithm. This performance enhancement occurs in terms of speed for a convenient choice of both the ratio γ = E'/E between the energy eigenvalues E' and E of the modified search Hamiltonian and the quantum mechanical overlap x between the source and the target states. Finally, we briefly discuss possible analytical improvements of our investigation together with its potential relevance in practical quantum engineering applications.
Coresets via Bilevel Optimization for Continual Learning and Streaming

Coresets are small data summaries that are sufficient for model training. They can be maintained online, enabling efficient handling of large data streams under resource constraints. However, existing constructions are limited to simple models such as k-means and logistic regression. In this work, we propose a novel coreset construction via cardinality-constrained bilevel optimization. We show how our framework can efficiently generate coresets for deep neural networks, and demonstrate its empirical benefits in continual learning and in streaming settings.
Quantum-enhanced stochastic phase estimation with the SU(1,1) interferometer

Quantum stochastic phase estimation has many applications in the precise measurement of various physical parameters. Similar to the estimation of a constant phase, there is a standard quantum limit for stochastic phase estimation, which can be obtained with the Mach–Zehnder interferometer and coherent input state. Recently, it has been shown that the stochastic standard quantum limit can be surpassed with nonclassical resources such as squeezed light. However, practical methods to achieve quantum enhancement in the stochastic phase estimation remain largely unexplored. Here we propose a method utilizing the SU(1,1) interferometer and coherent input states to estimate a stochastic optical phase. As an example, we investigate the Ornstein–Uhlenback stochastic phase. We analyze the performance of this method for three key estimation problems: prediction, tracking, and smoothing. The results show significant reduction of the mean square error compared with the Mach–Zehnder interferometer under the same photon number flux inside the interferometers. In particular, we show that the method with the SU(1,1) interferometer can achieve fundamental quantum scaling, achieve stochastic Heisenberg scaling, and surpass the precision of the canonical measurement.
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals

We study the fundamental problems of agnostically learning halfspaces and ReLUs under Gaussian marginals. In the former problem, given labeled examples $(\mathbf{x}, y)$ from an unknown distribution on $\mathbb{R}^d \times \{ \pm 1\}$, whose marginal distribution on $\mathbf{x}$ is the standard Gaussian and the labels $y$ can be arbitrary, the goal is to output a hypothesis with 0-1 loss $\mathrm{OPT}+\epsilon$, where $\mathrm{OPT}$ is the 0-1 loss of the best-fitting halfspace. In the latter problem, given labeled examples $(\mathbf{x}, y)$ from an unknown distribution on $\mathbb{R}^d \times \mathbb{R}$, whose marginal distribution on $\mathbf{x}$ is the standard Gaussian and the labels $y$ can be arbitrary, the goal is to output a hypothesis with square loss $\mathrm{OPT}+\epsilon$, where $\mathrm{OPT}$ is the square loss of the best-fitting ReLU. We prove Statistical Query (SQ) lower bounds of $d^{\mathrm{poly}(1/\epsilon)}$ for both of these problems. Our SQ lower bounds provide strong evidence that current upper bounds for these tasks are essentially best possible.
Minibatch vs Local SGD for Heterogeneous Distributed Learning

We analyze Local SGD (aka parallel or federated SGD) and Minibatch SGD in the heterogeneous distributed setting, where each machine has access to stochastic gradient estimates for a different, machine-specific, convex objective; the goal is to optimize w.r.t. the average objective; and machines can only communicate intermittently. We argue that, (i) Minibatch SGD (even without acceleration) dominates all existing analysis of Local SGD in this setting, (ii) accelerated Minibatch SGD is optimal when the heterogeneity is high, and (iii) present the first upper bound for Local SGD that improves over Minibatch SGD in a non-homogeneous regime.
Research on optical encryption system based on unequal modulus decomposition and polarized vortex optical holography

In this paper, we present an optical encryption system which uses the principle of unequal modulus decomposition and polarization vortex optical holography to implement encoding and decoding. Firstly, the original image is decomposed into two complex amplitude images by unequal modulus decomposition method, then their polarization states are changed by vortex beams irradiation, and finally they are encrypted in the photorefractive crystals by polarization multiplexing holographic technology. In decryption, the image can be reconstructed successfully when a series of correct keys are provided. The sensitivity of polarization state and the multiplexing performance of topological charge are also analyzed, proving the method has high security.
Logarithmic Pruning is All You Need

The Lottery Ticket Hypothesis is a conjecture that every large neural network contains a subnetwork that, when trained in isolation, achieves comparable performance to the large network. An even stronger conjecture has been proven recently: Every sufficiently overparameterized network contains a subnetwork that, at random initialization, but without training, achieves comparable accuracy to the trained large network. This latter result, however, relies on a number of strong assumptions and guarantees a polynomial factor on the size of the large network compared to the target function. In this work, we remove the most limiting assumptions of this previous work while providing significantly tighter bounds:the overparameterized network only needs a logarithmic factor (in all variables but depth) number of neurons per weight of the target subnetwork.
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly. To address the problem of propagating gradients through latent variables drawn from discrete distributions, we use their continuous relaxations known as Concrete distributions. We show that, on real datasets, our approach can outperform state-of-the-art Bayesian and non-Bayesian graph neural network algorithms on the task of semi-supervised classification in the absence of graph data and when the network structure is subjected to adversarial perturbations.
A Fast JPEG Image Compression Algorithm Based on DCT

With the advent of the information age, people rely more and more on the use of computers to obtain and process information. Computer system is faced with the transmission of multiple media file data. This paper introduces a fast JPEG image compression algorithm based on DCT. The algorithm introduces the process of image coding and decoding for JPEG. The encoding part of the image can process the BMP format image by JEPG, and compress it into a binary file for real-time storage. The image can be decompressed by the corresponding decoding program. In addition, in the process of image transmission, taking advantage of the fact that human vision is not sensitive to chroma, JPEG format can be used to encode static image, and the color RBG of JPEG image can be changed into brightness y, chroma Cr and CB, which can not only effectively reduce chroma data, but also achieve compression.
How to teach digital reading?

This paper offers a discussion of the knowledge, skills, and awareness involved in digital reading. Reading, in this paper, is used in the broader sense to include deriving meaning from media on a digital screen. This paper synthesises key ideas from existing studies and presents a taxonomy for the teaching of digital reading. The taxonomy includes the development of: 1) the knowledge of linear and deep reading strategies; 2) basic and critical information skills; and 3) a multimodal semiotic awareness. The goal of this paper is to unpack the specific knowledge and skills for digital reading which will support educators, including classroom teachers and librarians, on the aspects to pay attention to as students engage in digital reading. This paper argues that, in addition to equipping students with the knowledge of reading strategies and information skills, an awareness of how the various semiotic modes make meaning is fundamental to effective digital reading.
Zombie Awakening: Stealthy Hijacking of Active Domains through DNS Hosting Referral

In recent years, the security implication of stale NS records, which point to a nameserver that no longer resolves the domain, has been unveiled. Prior research studied the stale DNS records that point to expired domains. The popularity of DNS hosting services brings in a new category of stale NS records, which reside in the domain's zone (instead of the TLD zone) for an active domain. To the best of our knowledge, the security risk of this kind of stale NS record has never been studied before. In our research, we show that this new type of stale NS record can be practically exploited, causing a stealthier hijack of domains associated with the DNS hosting service. We also performed a large-scale analysis on over 1M high-profile domains, 17 DNS hosting providers and 12 popular public resolver operators to confirm the prevalence of this security risk. Our research further discovers 628 hijackable domains (e.g., 6 government entities and 2 payment services), 14 affected DNS hosting providers (e.g., Amazon Route 53), and 10 vulnerable public resolver operators (e.g., CloudFlare). Furthermore, we conducted an in-depth measurement analysis on them, thus providing a better understanding of this new security risk. Also, we explore the mitigation techniques that can be adopted by different affected parties.
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs

Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing centralized GraphSSC methods are impractical to solve many real-world graph-based problems, as collecting the entire graph and labeling a reasonable number of labels is time-consuming and costly, and data privacy may be also violated. Federated learning (FL) is an emerging learning paradigm that enables collaborative learning among multiple clients, which can mitigate the issue of label scarcity and protect data privacy as well. Therefore, performing GraphSSC under the FL setting is a promising solution to solve real-world graph-based problems. However, existing FL methods 1) perform poorly when data across clients are non-IID, 2) cannot handle data with new label domains, and 3) cannot leverage unlabeled data, while all these issues naturally happen in real-world graph-based problems. To address the above issues, we propose the first FL framework, namely GraphFL, for semi-supervised node classification on graphs. Our framework is motivated by meta-learning methods. Specifically, we propose two GraphFL methods to respectively address the non-IID issue in graph data and handle the tasks with new label domains. Furthermore, we design a self-training method to leverage unlabeled graph data. We adopt representative graph neural networks as GraphSSC methods and evaluate GraphFL on multiple graph datasets. Experimental results demonstrate that GraphFL significantly outperforms the compared FL baseline and GraphFL with self-training can obtain better performance.