Reviewerly/exHarmony huggingface dataset:
Securing Practical Quantum Communication Systems with Optical Power Limiters

Controlling the energy of unauthorized light signals in a quantum cryptosystem is an essential criterion for implementation security. Here, we propose a passive optical power limiter device based on thermo-optical defocusing effects providing a reliable power limiting threshold which can be readily adjusted to suit various quantum applications. In addition, the device is robust against a wide variety of signal variations (e.g. wavelength, pulse width), which is important for implementation security. Moreover, we experimentally show that the proposed device does not compromise quantum communication signals, in that it has only a very minimal impact (if not, negligible impact) on the intensity, phase, or polarization degrees of freedom of the photon, thus making it suitable for general communication purposes. To show its practical utility for quantum cryptography, we demonstrate and discuss three potential applications: (1) measurement-device-independent quantum key distribution with enhanced security against a general class of Trojan-horse attacks, (2) using the power limiter as a countermeasure against bright illumination attacks, and (3) the application of power limiters to potentially enhance the implementation security of plug-and-play quantum key distribution.
Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

This paper introduces Parallel Tacotron 2, a non-autoregressive neural text-to-speech model with a fully differentiable duration model which does not require supervised duration signals. The duration model is based on a novel attention mechanism and an iterative reconstruction loss based on Soft Dynamic Time Warping, this model can learn token-frame alignments as well as token durations automatically. Experimental results show that Parallel Tacotron 2 outperforms baselines in subjective naturalness in several diverse multi speaker evaluations. Its duration control capability is also demonstrated.
MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification

In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an easy-to-implement, simple but effective backbone for automatic speaker verification based on the Convolution-augmented Transformer (Conformer). The architecture of the MFA-Conformer is inspired by recent state-of-the-art models in speech recognition and speaker verification. Firstly, we introduce a convolution sub-sampling layer to decrease the computational cost of the model. Secondly, we adopt Conformer blocks which combine Transformers and convolution neural networks (CNNs) to capture global and local features effectively. Finally, the output feature maps from all Conformer blocks are concatenated to aggregate multi-scale representations before final pooling. We evaluate the MFA-Conformer on the widely used benchmarks. The best system obtains 0.64%, 1.29% and 1.63% EER on VoxCeleb1-O, SITW.Dev, and SITW.Eval set, respectively. MFA-Conformer significantly outperforms the popular ECAPA-TDNN systems in both recognition performance and inference speed. Last but not the least, the ablation studies clearly demonstrate that the combination of global and local feature learning can lead to robust and accurate speaker embedding extraction. We will release the code for future works to do comparison.
Linguistic data mining with complex networks: A stylometric-oriented approach

By representing a text by a set of words and their co-occurrences, one obtains a word-adjacency network being a reduced representation of a given language sample. In this paper, the possibility of using network representation to extract information about individual language styles of literary texts is studied. By determining selected quantitative characteristics of the networks and applying machine learning algorithms, it is possible to distinguish between texts of different authors. Within the studied set of texts, English and Polish, a properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the authorship attribution accuracy over 90%. A correspondence between the text authorship and the word-adjacency network structure can therefore be found. The network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simple lexical features. Additionally, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the writing style diversity is discussed; some differences between languages are observed.
A versatile quantum walk resonator with bright classical light

In a Quantum Walk (QW) the "walker" follows all possible paths at once through the principle of quantum superposition, differentiating itself from classical random walks where one random path is taken at a time. This facilitates the searching of problem solution spaces faster than with classical random walks, and holds promise for advances in dynamical quantum simulation, biological process modelling and quantum computation. Here we employ a versatile and scalable resonator configuration to realise quantum walks with bright classical light. We experimentally demonstrate the versatility of our approach by implementing a variety of QWs, all with the same experimental platform, while the use of a resonator allows for an arbitrary number of steps without scaling the number of optics. This paves the way for future QW implementations with spatial modes of light in free-space that are both versatile and scalable.
A Comparison Between Two Different Approaches for a Collaborative Mixed-Virtual Environment in Industrial Maintenance

Nowadays the market is becoming increasingly competitive, factories are required not onlyto enhance the product quality but also to reduce manufacturing and maintenance times. In an industrial context, modern factories are composed by many automated systems, such as industrial robots, which can perform different tasks. Although industrial robots are becoming more powerful and efficient, human workers are still required to accomplish different operations, such as training and maintenance procedures. The proposed research aims to assess a remote interaction system in an industrial training collaborative mixed-reality (CMR) environment. Aremote expert user is capable of explaining a training procedure to an unskilled local user. Remote and local users interact using different interaction systems: the remote operator gives assistance using an immersive Virtual Reality (VR) device, whereas the local user interacts usinga wearable Augmented Reality (AR) device. A comparison between an interaction based on the presence of a virtual human and one based on the use of abstract icons is proposed. In the firstcase, a virtual 3D representation of the remote technician is shown to the local user by using AR: the remote technician can pinpoint the components involved in the training procedure andthe local user can visualize the instructions through some animations of the virtual avatar. In the second case, the local user cannot see a 3D representation of the remote technician; on the other hand, different 3D models, such as animated icons, are displayed to the local operator through AR depending on the component pinpointed by the remote technician in the virtual environment. Each 3D icon should suggest to the local user which component has to be manipulated at the current step of the procedure. Preliminary results suggest that the interface that requires less resources to be developed and managed should be preferred. Although in no audio condition thevirtual avatar may improve the sense of presence of the remote technician, the use of abstract metaphors seems to be of primary importance to successfully complete an industrial task.
Craftec

We present Craftec, an extendable toolkit system focused on engaging older adults in maker technology by supporting their use of common crafting skills. Craftec is comprised of LilyPad Arduino-based toolkits to promote easier crafting with hard and soft mediums. We describe the system's design, a pilot test with 8 students, and 2 two-hour single session workshop evaluations by 17 older adults. We found Craftec facilitated the efficient integration of circuits within crafted items, including fewer short circuits as compared to a basic LilyPad Arduino kit. We discuss insights into creating an older adult toolkit focused on building and prototyping rather than facilitating STEM education.
Single-shot deflectometry for dynamic 3D surface profile measurement by modified spatial-carrier frequency phase-shifting method

We propose a new concept of single-shot deflectometry for real-time measurement of three-dimensional surface profile using a single composite pattern. To retrieve an accurate phase from one-frame composite pattern, we adapt the Fourier Transform (FT) method and the spatial carrier-frequency phase-shifting (SCPS) technique to our proposed deflectometry. Based on Lissajous figure and ellipse fitting method, we also correct the phase extraction error in SCPS technique by reducing the effect of background and modulation variations. The proposed technique is verified by comparing our measurement results with phase-shifting deflectometry, and the maximum difference between two measurement results is less than 30 nm rms. We also test the robustness to vibration and the measurement capability for dynamic object.
Decoding individual differences in STEM learning from functional MRI data

Abstract Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization

Normalization techniques such as Batch Normalization have been applied very successfully for training deep neural networks. Yet, despite its apparent empirical benefits, the reasons behind the success of Batch Normalization are mostly hypothetical. We here aim to provide a more thorough theoretical understanding from a classical optimization perspective. Our main contribution towards this goal is the identification of various problem instances in the realm of machine learning where Batch Normalization can provably accelerate optimization. We argue that this acceleration is due to the fact that Batch Normalization splits the optimization task into optimizing length and direction of the parameters separately. This allows gradient-based methods to leverage a favourable global structure in the loss landscape that we prove to exist in Learning Halfspace problems and neural network training with Gaussian inputs. We thereby turn Batch Normalization from an effective practical heuristic into a provably converging algorithm for these settings. Furthermore, we substantiate our analysis with empirical evidence that suggests the validity of our theoretical results in a broader context.
Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g., video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data, while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data are arranged into sets, called bags, which are weakly labeled. Most AL methods focus on single-instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance AL (MIAL). The aggregated informativeness method identifies the most informative instances based on classifier uncertainty and queries bags incorporating the most information. The other proposed method, called cluster-based aggregative sampling, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single-instance AL methods.
ARENA—Augmented Reality to Enhanced Experimentation in Smart Warehouses

The current industrial scenario demands advances that depend on expensive and sophisticated solutions. Augmented Reality (AR) can complement, with virtual elements, the real world. Faced with this features, an AR experience can meet the demand for prototype testing and new solutions, predicting problems and failures that may only exist in real situations. This work presents an environment for experimentation of advanced behaviors in smart factories, allowing experimentation with multi-robot systems (MRS), interconnected, cooperative, and interacting with virtual elements. The concept of ARENA introduces a novel approach to realistic and immersive experimentation in industrial environments, aiming to evaluate new technologies aligned with the Industry 4.0. The proposed method consists of a small-scale warehouse, inspired in a real scenario characterized in this paper, managing by a group of autonomous forklifts, fully interconnected, which are embodied by a swarm of tiny robots developed and prepared to operate in the small scale scenario. The AR is employed to enhance the capabilities of swarm robots, allowing box handling and virtual forklifts. Virtual laser range finders (LRF) are specially designed as segmentation of a global RGB-D camera, to improve robot perception, allowing obstacle avoidance and environment mapping. This infrastructure enables the evaluation of new strategies to improve manufacturing productivity, without compromising the production by automation faults.
Sub-shot-noise-limited fiber-optic quantum receiver

We experimentally demonstrate a quantum receiver based on the Kennedy scheme for discrimination between two phase-modulated weak coherent states. The receiver is assembled entirely from standard fiber-optic elements and operates at a conventional telecom wavelength of 1.55 $\ensuremath{\mu}\text{m}$. The local oscillator and the signal are transmitted through different optical fibers, and the displaced signal is measured with a high-efficiency superconducting nanowire single-photon detector. We show the discrimination error rate is two times below that of a shot-noise-limited receiver with the same system detection efficiency.
Stacked borrows: an aliasing model for Rust

Type systems are useful not just for the safety guarantees they provide, but also for helping compilers generate more efficient code by simplifying important program analyses. In Rust, the type system imposes a strict discipline on pointer aliasing, and it is an express goal of the Rust compiler developers to make use of that alias information for the purpose of program optimizations that reorder memory accesses. The problem is that Rust also supports unsafe code, and programmers can write unsafe code that bypasses the usual compiler checks to violate the aliasing discipline. To strike a balance between optimizations and unsafe code, the language needs to provide a set of rules such that unsafe code authors can be sure, if they are following these rules, that the compiler will preserve the semantics of their code despite all the optimizations it is doing. In this work, we propose Stacked Borrows , an operational semantics for memory accesses in Rust. Stacked Borrows defines an aliasing discipline and declares programs violating it to have undefined behavior , meaning the compiler does not have to consider such programs when performing optimizations. We give formal proofs (mechanized in Coq) showing that this rules out enough programs to enable optimizations that reorder memory accesses around unknown code and function calls, based solely on intraprocedural reasoning. We also implemented this operational model in an interpreter for Rust and ran large parts of the Rust standard library test suite in the interpreter to validate that the model permits enough real-world unsafe Rust code.
Deep Object Co-Segmentation via Spatial-Semantic Network Modulation

Object co-segmentation is to segment the shared objects in multiple relevant images, which has numerous applications in computer vision. This paper presents a spatial and semantic modulated deep network framework for object co-segmentation. A backbone network is adopted to extract multi-resolution image features. With the multi-resolution features of the relevant images as input, we design a spatial modulator to learn a mask for each image. The spatial modulator captures the correlations of image feature descriptors via unsupervised learning. The learned mask can roughly localize the shared foreground object while suppressing the background. For the semantic modulator, we model it as a supervised image classification task. We propose a hierarchical second-order pooling module to transform the image features for classification use. The outputs of the two modulators manipulate the multi-resolution features by a shift-and-scale operation so that the features focus on segmenting co-object regions. The proposed model is trained end-to-end without any intricate post-processing. Extensive experiments on four image co-segmentation benchmark datasets demonstrate the superior accuracy of the proposed method compared to state-of-the-art methods. The codes are available at http://kaihuazhang.net/.
RubikAuth: Fast and Secure Authentication in Virtual Reality

There is a growing need for usable and secure authentication in virtual reality (VR). Established concepts (e.g., 2D graphical PINs) are vulnerable to observation attacks, and proposed alternatives are relatively slow. We present RubikAuth, a novel authentication scheme for VR where users authenticate quickly by selecting digits from a virtual 3D cube that is manipulated with a handheld controller. We report two studies comparing how pointing using gaze, head pose, and controller tapping impacts RubikAuth's usability and observation resistance under three realistic threat models. Entering a four-symbol RubikAuth password is fast: 1.69 s to 3.5 s using controller tapping, 2.35 s to 4.68 s using head pose, and 2.39 s to 4.92 s using gaze and highly resilient to observations; 97.78% to 100% of observation attacks were unsuccessful. Our results suggest that providing attackers with support material contributes to more realistic security evaluations.
RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling-Based Video Coding

Integrating deep learning techniques into the video coding framework gains significant improvement compared to the standard compression techniques, especially applying super-resolution (up-sampling) to down-sampling based video coding as post-processing. However, besides up-sampling degradation, the various artifacts brought from compression make super-resolution problem more difficult to solve. The straightforward solution is to integrate the artifact removal techniques before super-resolution. However, some helpful features may be removed together, degrading the super-resolution performance. To address this problem, we proposed an end-to-end restoration-reconstruction deep neural network (RR-DnCNN) using the degradation-aware technique, which entirely solves degradation from compression and sub-sampling. Besides, we proved that the compression degradation produced by Random Access configuration is rich enough to cover other degradation types, such as Low Delay P and All Intra, for training. Since the straightforward network RR-DnCNN with many layers as a chain has poor learning capability suffering from the gradient vanishing problem, we redesign the network architecture to let reconstruction leverages the captured features from restoration using up-sampling skip connections. Our novel architecture is called restoration-reconstruction u-shaped deep neural network (RR-DnCNN v2.0). As a result, our RR-DnCNN v2.0 outperforms the previous works and can attain 17.02% BD-rate reduction on UHD resolution for all-intra anchored by the standard H.265/HEVC. The source code is available at https://minhmanho.github.io/rrdncnn/.
Convolutional neural networks for relevance feedback in content based image retrieval

Abstract Given the great success of Convolutional Neural Network (CNN) for image representation and classification tasks, we argue that Content-Based Image Retrieval (CBIR) systems could also leverage on CNN capabilities, mainly when Relevance Feedback (RF) mechanisms are employed. On the one hand, to improve the performances of CBIRs, that are strictly related to the effectiveness of the descriptors used to represent an image, as they aim at providing the user with images similar to an initial query image. On the other hand, to reduce the semantic gap between the similarity perceived by the user and the similarity computed by the machine, by exploiting an RF mechanism where the user labels the returned images as being relevant or not concerning her interests. Consequently, in this work, we propose a CBIR system based on transfer learning from a CNN trained on a vast image database, thus exploiting the generic image representation that it has already learned. Then, the pre-trained CNN is also fine-tuned exploiting the RF supplied by the user to reduce the semantic gap. In particular, after the user’s feedback, we propose to tune and then re-train the CNN according to the labelled set of relevant and non-relevant images. Then, we suggest different strategies to exploit the updated CNN for returning a novel set of images that are expected to be relevant to the user’s needs. Experimental results on different data sets show the effectiveness of the proposed mechanisms in improving the representation power of the CNN with respect to the user concept of image similarity. Moreover, the pros and cons of the different approaches can be clearly pointed out, thus providing clear guidelines for the implementation in production environments.